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In this episode, I sit down with Dr. Nina Kottler, Associate Chief Medical Officer of Clinical AI at Radiology Partners, to dive into the evolving role of AI in radiology and how it can shape the future of digital pathology. Dr. Kottler shares her unique journey, expertise, and practical frameworks for implementing AI that enhance patient care and streamline diagnostic workflows.
Episode Highlights and Key Moments:
[00:00:45] Introduction to Dr. Nina Kottler
Dr. Kottler discusses her background in applied mathematics, her journey into medicine, and her work at Radiology Partners, where she combines clinical practice with AI innovation.[00:04:30] Breaking Down Complex Problems in AI
Nina explains her approach to tackling large clinical challenges by breaking them down into manageable parts, a method that’s essential for developing and optimizing AI solutions.[00:08:15] The Role of Data Orchestration
We dig into “data orchestration” and how ensuring data is aligned with the right AI model is key to producing accurate and reliable clinical outcomes.[00:11:45] Life Cycle of an Exam in Radiology
Nina takes us through each step in the radiology workflow—from the initial patient consultation to reporting—and highlights how AI can streamline and enhance each phase.[00:17:00] Evolution of AI Models in Healthcare
We explore how AI has evolved, from early CAD systems to today’s multimodal and transformer models, and the exciting possibilities they bring to both radiology and pathology.[00:23:20] Addressing the Lag in AI Adoption in Healthcare
We discuss the challenge of keeping up with AI advancements while balancing patient safety, regulatory standards, and the need for reliability in clinical settings.[00:27:50] Frameworks for Reducing Variability and Improving Accuracy
Nina shares actionable frameworks that Radiology Partners uses to reduce variability and improve diagnostic precision—strategies that pathology can learn from.[00:32:40] AI in Workflow Optimization: Where It Has Real Impact
We discuss specific use cases in clinical workflows that show where AI can bring the greatest value, especially in enhancing patient care through optimized processes.[00:36:50] The Power of Multimodal AI and Vision-Language Models
Combining large language models with computer vision is moving diagnostics closer to comprehensive, AI-driven care—a promising development we explore in depth.[00:42:15] The Future of Agents in AI
We dive into the concept of “agents” in AI and how these systems may soon coordinate multiple models for more complex and precise clinical analyses.[00:48:10] Where to Learn More about Dr. Nina Kottler’s Work
Nina shares where you can catch her upcoming talks and presentations, plus resources for staying updated on the latest in AI for radiology and digital pathology.If you're a pathologist, radiologist, or healthcare professional curious about AI’s impact on diagnostics, this episode is packed with practical guidance on integrating AI into clinical workflows. Join us as we explore how AI is shaping the future of radiology and pathology!
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Welcome back to the DigiPath Digest, fresh from PathVision!
In this episode we will dive into the latest updates from the PathVision conference, covering trends in AI-driven diagnostics, the expansion of digital pathology into primary care, and the exciting new frontier of glassless pathology.Join me as I recap the highlights of PathVision and the latest updates from the digital pathology literature, including discussions on:
AI Integration in Pathology: Learn how AI is advancing breast cancer diagnostics with tools like Ki-67 scoring models and multi-label AI for mammography, aimed at reducing unnecessary biopsies.Global Health & Digital Microscopy: Hear about innovative projects from Sweden and Finland focused on AI-supported digital microscopy in primary healthcare labs, bringing accessible diagnostics to underserved areas.Glassless Pathology with MUSE: Discover how glassless pathology is changing tissue imaging with MUSE (Microscopy with UV Surface Excitation), enabling diagnostics without the need for traditional glass slides. Dr. Zuraw breaks down what this means for future pathology workflows.Plus, a shout-out to the vendors and partners making these advancements possible, and insights from Dr. Zuraw’s conversations with digital pathology trailblazers from around the globe, including new developments from Asia in digital pathology education and technology.
Timestamps:
[0:00] PathVision Highlights & Global Attendees[5:15] AI in Diagnostic Workflows: Dr. Anil Parwani’s “Pathology Train Ride”[12:30] Moving Beyond Narrow AI: Multimodal and Foundational Models[18:45] Glassless Pathology: A New Frontier with MUSE Microscopy[25:10] Integrating Digital Microscopy in Global Health Labs[32:00] Breast Cancer Month: New Advances in AI for Diagnostics[42:00] One Health & AI for Disease Detection in Primary Care[48:30] Special Interviews: Jun Fukuoka and Asian Society of Digital PathologyLinks and Resources:
Subscribe to Digital Pathology Podcast on YouTubePathology News Signify Research Monthly RecapYouTube version of this episodePublications Discussed Today:
📝 AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review
🔗https://pubmed.ncbi.nlm.nih.gov/39486020/
📝 Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer
🔗https://pubmed.ncbi.nlm.nih.gov/39478421/
📝A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis
🔗https://pubmed.ncbi.nlm.nih.gov/39477432/
📝 A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol
🔗 https://pubmed.ncbi.nlm.nih.gov/39466830/
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In this episode, I meet with Adam Cole, MD, and Jason Camilletti about how digital pathology transforms the field. Adam, the CEO of TruCore Pathology, and Jason, the CEO of PathNet Labs, share their unique journeys from the military to becoming digital pathology leaders. We explore their experiences, challenges, and innovations in integrating AI and digital tools into their practices.
Key Topics Discussed:
[00:00:00] Introduction to AI in Pathology[00:01:00] Adam and Jason’s Military Backgrounds[00:05:00] Adam’s Story of Becoming a Mobile Pathologist[00:10:00] The Move to Fully Digital Pathology[00:14:30] AI’s Role in Pathology[00:20:00] Challenges in Implementing Digital Pathology[00:25:00] Improving Patient Outcomes with Digital Tools[00:29:00] Digital Pathology’s Impact on Patient Care[00:38:00] Using AI for Quantifying Tumor Volume[00:40:00] The Role of AI in Enhancing Diagnostics
Adam and Jason emphasize the immense potential of AI in pathology, but also the need for thoughtful integration. The future of pathology lies in using digital tools to provide faster, more accurate diagnoses while maintaining the critical human element. Tune in to learn how AI is reshaping the field and what it means for both pathologists and patients.
TruCore websitePathNet Website
THIS EPISODE'S RESOURCES:
The Evolution of Digital Pathology – from Improved Histology Quality to Fair Use of Pathology Data w/ Matthew O. Leavitt, DDx FoundationAchieving work-life balance in medicine as a pathologist with digital pathology w/ Todd Randolph, MD
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What does the FDA jurisdiction for LDTs mean for the labs? Do they need to worry? How do they need to change the way they operate?
In this episode, I talk with Dr. Thomas Nifong, a clinical pathologist and VP of CDX operations at Acrovan Therapeutics, about the recent FDA ruling on laboratory-developed tests (LDTs) issued on May 6th, 2024. We discuss the implications of considering LDTs as medical devices, requiring regulation, and explore the authority of FDA versus CLIA. The conversation also covers historical contexts, practical implications of regulatory changes, and the roles of organizations like CAP, ACLA, and AMP in legal challenges against the FDA. We dive into the differences in requirements between CLIA and FDA, New York's alternative approval route, and potential impacts on lab operations and compliance. Join us for an insightful conversation filled with essential information for those in the field of molecular pathology.
00:00 Introduction and Special Guest Announcement
00:24 FDA's New Rule on Laboratory Developed Tests (LDTs)
01:58 Recording the Podcast: A Casual Lunch Conversation
03:47 Understanding FDA's Authority Over Medical Devices
08:07 Disputes and Legal Challenges
12:03 Practical Implications and Industry Reactions
12:47 Understanding FDA's Focus: Safety and Efficacy
14:11 The Role of CMS and Medical Necessity
14:48 Congressional Involvement and Legal Authority
16:06 Impact on Labs and Future LDTs
18:33 Quality Systems and Compliance
20:16 Modifications and Software Updates
21:16 Conclusion and Next StepsSupport the show
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In this episode, I had a fascinating conversation with Candice Chu, DVM, PhD, DACVP, about how artificial intelligence (AI) is reshaping veterinary diagnostics and education. Candice, a clinical pathologist and educator at Texas A&M, is using AI tools like ChatGPT to improve efficiency in clinical workflows and academic processes. We explored the practical applications of AI, ethical concerns, and its future impact on veterinary medicine.
[00:00:00] Introduction to AI in Veterinary Education and Diagnostics
Key Topics Discussed:
I ask Candice how AI is changing veterinary education and diagnostics, and she explains how AI is boosting efficiency in both areas.[00:01:00] Candice’s Journey in Veterinary Medicine
Candice shares her journey from Taiwan to the U.S., her career in veterinary pathology, and becoming an educator at Texas A&M.[00:05:00] Custom GPT Model for Clinical Pathology
Candice describes the development of her custom GPT model for clinical pathology and its role in improving diagnostic efficiency.[00:10:00] AI Tools for Academic and Clinical Efficiency
We talk about how AI tools reduce repetitive tasks, giving professionals more time for critical thinking and decision-making.[00:14:30] Ethical Concerns When Using AI in Veterinary Medicine
Candice emphasizes the ethical responsibility of using AI, highlighting the importance of human judgment in AI-assisted diagnostics.[00:20:00] How Veterinary Students Can Leverage AI
Candice shares tips on how students can use AI to enhance learning, from simplifying research to generating case questions.[00:29:00] AI’s Role in Academic Writing and Veterinary Practice
We discuss how AI tools streamline academic writing and research, and how AI will continue shaping veterinary practice in the future.[00:39:00] Critical Thinking and AI in Veterinary Medicine
Candice and I conclude by discussing how critical thinking and professional responsibility are essential when using AI tools.Candice highlighted the transformative role AI can play in both veterinary education and diagnostics, improving efficiency while requiring responsible use. While AI tools like ChatGPT offer many benefits, the human element—our critical thinking and judgment—remains crucial in ensuring accurate results and ethical practices.
This episode provides practical insights on how veterinary professionals, educators, and students can harness AI to streamline workflows and improve diagnostic accuracy. Be sure to listen to the full conversation for actionable tips on integrating AI into your practice!
About Dr. Candice Chu (Including her social media and achievements)Candice's PaperUndermind AIYoutube Episode of this Episode
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In this episode, Dr. Richard Fox shares how AI is transforming veterinary diagnostics. From his early career to the world of AI, Dr. Fox offers practical insights into the challenges, opportunities, and innovations that AI brings to pathology. Tune in to learn how AI is enhancing workflow efficiency, diagnostic precision, and the future direction of veterinary pathology.
[00:00] Introduction – Introduction to Dr. Richard Fox and his expertise in veterinary pathology and AI.
[03:00] Dr. Fox’s Career Journey – His shift from veterinary practice to pathology and AI.
[08:00] Entering the AI Space – How Dr. Fox became involved in AI, including his work with Aiforia.
[15:00] AI in Diagnostics – AI’s impact on diagnostic workflows and speeding up tasks.
[22:00] Quality Control in AI Models – Ensuring AI model accuracy and the importance of data consistency.
[28:00] AI Model Validation Challenges – Overcoming issues with model validation and retraining.
[35:00] Integrating AI into Workflows – How AI fits into veterinary pathology workflows and practical considerations.
[40:00] Future of AI in Pathology – Predictions on the future trends in AI and on-premises diagnostics.
[50:00] Common Questions About AI – Addressing concerns like AI replacing pathologists and optimizing workflows.
[58:00] Conclusion – Key takeaways and how to get started with AI in veterinary diagnostics.
The Episodes Resources:
Contact Aiforia
Richard Fox's LinkedIn Profile
Richard Fox's EmailSupport the show
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In this 14th episode of DigiPath Digest, I introduce a new course on AI in pathology, designed to help pathologists understand and confidently navigate AI technologies.
The episode focuses on various research studies that highlight the integration and effectiveness of AI in pathology, particularly in colorectal biopsies and kidney transplant biopsies, emphasizing the importance of seamless workflow integration.
You will also learn about challenges in manual assessment of tumor-infiltrating lymphocytes and HER2 expression in breast cancer. I advocate for more consistent and precise AI-driven approaches.
And there an opportunity for a discounted beta test of the new AI course.
00:00 Welcome to DigiPath Digest #1400:24 New AI Course Announcement
01:51 Deep Learning in Colorectal Biopsies
09:17 AI in Kidney Biopsy Evaluation
16:12 Automated Scoring of Tumor Infiltrating Lymphocytes
24:22 AI for HER2 Expression in Breast Cancer
31:13 Conclusion and Course Details
THIS EPISODE'S RESOURCES
📰 A deep learning approach to case prioritisation of colorectal biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39360579/
📰 Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39356416/
📰 Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review
🔗 https://pubmed.ncbi.nlm.nih.gov/39350098/
📰 Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer
🔗 https://pubmed.ncbi.nlm.nih.gov/39350085/
▶️ YouTube Version of this Episode:
🔗 https://www.youtube.com/live/jkT8dTxelt4?si=xT6MNH7O4HuUnAN6
📕 Digital Pathology 101 E-book
🔗https://digitalpathology.club/digital-pathology-beginners-guide-notification
🤖 "Pathology's AI Makeover" Online Course 50% OFF
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Good morning, digital pathology trailblazers! Welcome to another exciting exploration of digital pathology and AI. I’m thrilled to have our global community here with us today from so many different time zones. Before we dive into today's content, a quick note: my equipment is being a bit finicky, but that’s life in the digital world!
Integrating Image Analysis with AI
Let's kick off with a recap of some recent updates. Yesterday, I had the privilege of presenting to a mixed group at Cincinnati Children’s Hospital. We discussed AI in image analysis, an essential tool bridging radiology and pathology as these fields rapidly evolve with new technologies like foundation models and large language models. A diverse audience—ranging from radiologists to pathologists—prompted me to adapt my presentation style on the spot. It was a dynamic discussion about the advancements in healthcare that shared perspectives from both sides.
Lymphovascular Invasion: A Case Study
Our first paper today focuses on a deep learning model for identifying lymphovascular invasion (LVI) in lung adenocarcinoma. This significant prognostic factor is crucial for advancing diagnostic consistency and reliability. Unlike broad foundation models, this work engages with dedicated image analysis applications targeting specific diagnostic challenges. The study demonstrated reduced pathologist evaluation time by nearly 17% and even more in complex cases, aligning with previous findings that AI enhances efficiency by around 21%.
AI Collaborations: Human and Veterinary Pathology
Next, we delve into a collaborative effort between human and veterinary pathologists, emphasizing the promise of AI integration in telepathology and digital pathology. These fields are converging to enhance information exchange, teaching, and research. I’m particularly excited about this paper due to my own veterinary pathology background and the potential it offers for both educational and clinical practices.
Spatial Profiling and Immuno-Oncology
We then journey into the intricate landscape of immuno-oncology with a study on PD-1 and PD-L1 in osteosarcoma microenvironments. Utilizing deep learning and multiplex fluorescence immunohistochemistry, researchers highlighted the spatial orchestration of these markers, providing insights into potential immunotherapeutic strategies. This work is an exemplar of how AI can illuminate complex biological landscapes, offering a path for future therapies.
Conclusion
Thank you all for joining this vibrant discussion. Whether you’re tuning in from early morning in Atlanta or late at night in Algeria, your engagement enriches our learning experience. Keep an eye out for more content and upcoming courses designed to unpack these groundbreaking developments in AI and digital pathology.
Until next time, keep blazing trails in digital pathology!
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The episode explores the concept of blind review, a process designed to eliminate hindsight bias by allowing medical experts to evaluate cases without knowing the outcome or the hiring party.
Stephanie's LinkedIn ProfileThe Blind Review Website
Stephanie Franckewitz, JD, MBA, founder of Blind Review, discusses its application in legal cases, particularly for digital pathology and radiology. By providing an unbiased expert opinion, blind review aids the defense and plaintiff parties in court, increasing the chances of a favorable verdict.
Stephanie outlines her journey from a medical malpractice defense lawyer to starting Blind Review and highlights the potential for digital pathology to revolutionize the legal process, reduce bias, and improve case outcomes.
Collaboration with platforms like PathPresenter enables pathology slides to be reviewed efficiently and effectively within a legal context. This approach benefits both defendants and plaintiffs by ensuring objective evaluations and enhancing the credibility of expert testimonies in trials.
00:00 Introduction to Blind Review
01:19 The Role of Digital Pathology in Legal Cases
02:16 Stephanie Franke Reid's Journey
07:19 Challenges in Traditional Expert Reviews
10:09 Implementing Blind Review in Pathology
18:16 Collaboration with PathPresenter
25:43 Streamlining the Legal Process with Digital Pathology
26:51 Collaborative Tools for Legal Experts
27:20 Path Presenter: A Game Changer for Attorneys
28:17 Understanding Pathology for Juries
29:20 Streamlining Case Preparation with Path Presenter
31:54 Setting Up a Blind Review Process
35:38 The Gold Standard of Blind Review
41:53 Impact of Blind Review on Legal Outcomes
49:49 Empowering Legal and Medical Professionals
54:50 Conclusion and Call to Action - contact Stephanie
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In this episode, I celebrate another milestone of the Digital Pathology Place YouTube channel that was achieved thanks to you, my digital pathology trailblazer, reflecting on its journey since its inception in 2019.
The YouTube version of "AI in Pathology" first videoThe updated "Artificial Intelligence in Pathology" video (coming soon in podcast version)
I delve into the developments in digital pathology, focusing on the first video I ever published on YouTube about AI in pathology, highlighting trends, tools, and challenges in the field.
The video was based on a presentation I gave on the day I got engaged, so if you want to know the whole story listen in.
I explain key concepts like
- artificial intelligence,
- machine learning, and
- deep learning, and discuss
- How could AI eventually support pathology practice despite current challenges?
00:00 Welcome and AI Co-Host Feedback
00:19 YouTube Monetization Milestone
01:18 Reflecting on the First Video
02:47 Special Day and Personal Story
05:06 Introduction to AI in Pathology
07:26 AI Terminology and Concepts
13:17 Current Status of AI in Pathology
17:33 Challenges and Future of AI in Pathology
22:42 Conclusion and Call to Action
23:30 Updates and Future Plans
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In this episode of DigiPath Digest you will learn about the development of AI models for glaucoma screening using fundus images, the use of AI in detecting metastatic deposits in colorectal cancer, and leveraging immunofluorescence data to reduce pathologist annotation requirements.
Dr. Aleks also invited two AI Co-hosts and shared personal reflections on AI's role in the industry and invites feedback from listeners on AI-generated content.
00:00 Introduction to the Livestream Disaster
00:24 AI to the Rescue: Enhancing Audio Quality
00:38 Meet the AI Co-Hosts
01:04 Welcome to the Digital Pathology Podcast
01:30 Technical Difficulties and Audience Interaction
02:49 Exploring AI in Veterinary Medicine
04:34 Hybrid Convolutional Neural Network for Glaucoma Screening
07:49 Model for Detecting Metastatic Deposits in Lymph Nodes
11:23 Leveraging Immunofluorescence Data for Lung Tumor Segmentation
18:05 AI-Generated Content and Future Plans
21:37 AI Co-Hosts Take Over
32:42 Conclusion and Audience Feedback
TODAY'S EPISODES RESOURCES
📰 Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images
🔗https://pubmed.ncbi.nlm.nih.gov/39301801/
📰 Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network
🔗https://pubmed.ncbi.nlm.nih.gov/39300922/
📰 Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
🔗https://pubmed.ncbi.nlm.nih.gov/39298885/
📰 Obesity-Associated Breast Cancer: Analysis of Risk Factors and Current Clinical Evaluation
🔗 https://pubmed.ncbi.nlm.nih.gov/39287872/
📰 Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images
🔗 https://pubmed.ncbi.nlm.nih.gov/39285483/
📰 Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning
🔗 https://pubmed.ncbi.nlm.nih.gov/39284813/
📰 Bayesian Landmark-based Shape Analysis of Tumor Pathology Images
🔗 https://pubmed.ncbi.nlm.nih.gov/39280355/
📰 Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol
🔗 https://pubmed.ncbi.nlm.nih.gov/39280257/
📰 Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy
🔗 https://pubmed.ncbi.nlm.nih.gov/39277586/
📰 Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study
🔗 https://pubmed.ncbi.nlm.nih.gov/39019434/Support the show
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In this episode of DigiPath Digest, we review the latest AI developments in digital pathology described in the literature. I explore how AI is pushing the boundaries of metastasis detection, breast cancer treatment predictions, lung cancer research trends, and the creation of pathology foundation models.
Episode Breakdown:
00:00 – Welcome & Introduction00:36 – Sentinel Node Metastasis Detection: A discussion on the development of an AI model that can detect sentinel node metastasis in melanoma with accuracy comparable to that of pathologists. The model aids in distinguishing between nodal metastasis and intra-nodal nevus, which is crucial for accurate staging in melanoma patients.05:01 – Predicting Breast Cancer Treatment Response: A cross-modal AI model that integrates pathology images and ultrasound data is explored. This model is designed to predict a breast cancer patient’s response to neoadjuvant chemotherapy, providing personalized insights that can guide treatment decisions.09:59 – Global Trends in AI and Lung Cancer Pathology: This section reviews a bibliometric study that analyzed global research trends in AI-based digital pathology for lung cancer over the past two decades. The study highlights the need for increased collaboration between institutions and countries to further AI advancements in this area.13:30 – Pathology Foundation Models: An in-depth look at a new foundation model in pathology, designed to generalize across various diagnostic tasks. This model shows significant promise in cancer diagnosis and prognosis prediction, outperforming traditional deep learning methods by addressing domain shifts across different datasets.20:08 – Domain Shifts in AI Models: A brief discussion on the impact of domain shifts, such as variations in staining protocols and patient populations, on the performance of AI models in pathology. Strategies for mitigating these challenges are highlighted.29:09 – Faster Annotation in Pathology: The episode concludes with a review of a study comparing manual and semi-automated annotation methods. The semi-automated approach significantly reduces the time required for annotating whole slide images, offering a more efficient solution for pathologists.Resources Mentioned:
📰 Sentinel Node Metastasis Detection in Melanoma
🔗 https://pubmed.ncbi.nlm.nih.gov/39238597/
📰 Cross-Modal Deep Learning for Breast Cancer Response
🔗 https://pubmed.ncbi.nlm.nih.gov/39237596/
📰 Global Bibliometric Mapping in Lung Cancer Pathology
🔗 https://pubmed.ncbi.nlm.nih.gov/39233894/
📰 CHIEF Foundation Model for Cancer Diagnosis
🔗 https://pubmed.ncbi.nlm.nih.gov/39232164/
📰 Improving Annotation Processes in Pathology
🔗 https://pubmed.ncbi.nlm.nih.gov/39231887/Support the show
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Welcome to the 10th edition of the DigiPath Digest. Today, we discuss essential updates including the free availability of my 'Digital Pathology 101' book and the podcast now accessible on YouTube and YouTube Music. We dive deep into the weekly abstract, focusing on advancements such as sex-specific histopathological models for gliomas, leukocyte identification tools, and automated Gleason grading for prostate cancer. We also explore the potential of SciSpace, an AI tool for interacting with scientific papers. Interspersed with live interaction, we discuss the importance of consistency in histopathological grading and the challenges faced by pathologists. J
00:00 Introduction and Announcements
00:55 Live Interaction and Updates
05:01 Abstract Review: High-Grade Gliomas
11:45 Abstract Review: Leukocyte Identification Tool
13:24 Abstract Review: Gleason Grading in Prostate Cancer
16:31 Abstract Review: HER2 Low Prediction in Breast Cancer
24:01 Event Announcements and Closing Remarks
THIS EPISODES RESOURCES:
📰 Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning
🔗https://pubmed.ncbi.nlm.nih.gov/39178259/
📰 A Digital Tool Supporting Pathology Practice and Identifying Leucocytes
🔗https://pubmed.ncbi.nlm.nih.gov/39176939/
📰 Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
🔗https://pubmed.ncbi.nlm.nih.gov/39176576/
📰 Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides
🔗https://pubmed.ncbi.nlm.nih.gov/39160593/
▶️ YouTube Version of this Episode:
🔗 https://www.youtube.com/live/06QXmwojxDE?si=q59PjGkHbXCUFhwI
📕 Digital Pathology 101 E-book
🔗https://digitalpathology.club/digital-pathology-beginners-guide-notificationShow less
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Today my guest is Danielle Brown, a fellow veterinary pathologist, the General Manager at Charles River Laboratories Reno, Nevada, and a pioneer in the use of image analysis for toxicologic pathology. Together, we explored the ever-evolving role of image analysis in preclinical studies and how it enhances, rather than replaces, the expertise of pathologists.
This conversation is a deep dive into the intersection of pathology and technology, showcasing how image analysis is revolutionizing preclinical research. We also discuss the future of this technology and its implications for the industry.
Join us as we navigate the intricacies of image analysis, share insights on the collaborative process between pathologists and image analysis scientists, and look ahead to the exciting advancements on the horizon.
Key Discussion Points:
[00:00:00] Introduction and Guest Welcome:Introducing Danielle Brown and her significant contributions to the field of toxicologic pathology.[00:02:46] The Role of Image Analysis in Preclinical Drug Development:Why image analysis is crucial for accurate and efficient evaluations in preclinical studies.[00:03:23] Challenges and Limitations of Visual Analysis:Discussing the limitations of visual analysis and how image analysis overcomes these challenges.[00:08:06] Pathologist and Image Analysis Collaboration:The importance of collaboration between pathologists and image analysis scientists to ensure accurate data interpretation.[00:13:00] Efficiency and Cost of Image Analysis vs. Pathologist Scoring:Comparing the efficiency, cost, and consistency between image analysis and traditional pathologist scoring methods.[00:15:18] Validation and Qualification of Image Analysis Algorithms:The process of validating image analysis algorithms to ensure they meet regulatory standards in a GLP environment.[00:19:54] GLP Compliance and Regulatory Considerations:How Charles River ensures GLP compliance in their image analysis processes, making them suitable for regulatory submissions.[00:23:27] Method Development for Specific Stains and Techniques:Approaching projects that require new method development or specialized procedures.[00:27:46] Future of Image Analysis in Pathology:Danielle’s insights into the future of image analysis and how emerging technologies will shape the field.This episode is packed with valuable insights and practical advice for anyone involved in preclinical research or interested in the integration of image analysis in pathology. Danielle’s expertise and our discussion provide a roadmap for leveraging image analysis to increase evaluation efficiency and the granularity of your data.
THIS EPISODE'S RESOURCES:
📄 The paper Aleks and Danielle co-authored: "Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology"📄 Learn more about GLP-compliant tissue image analysis at Charles River Laboratories.
▶️ Watch the full episode here: Image Analysis Enhances Pathology Evaluation of Preclinical Studies, not Replaces it.
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In this episode, we celebrate the 100th edition of the Digital Pathology Podcast!
Thank you so much for being part of this journey!
You are my Digital Pathology Trailblazers and I prepared a Digital Pathology Trailblazer manifesto for us!
This is the 9th edition of DigiPath Digest, and we are attracting more and more people to this series.
I am also working on a new YouTube digital pathology course and am offering the first 100 enrollments for free in exchange for feedback.
During today's episode, we cover several papers including research on AI for predicting post-operative liver metastasis, validation of AI-based breast cancer risk stratification models, AI applications in clinical microbiology, advances in parasitology diagnostics, AI for retinal assessment, and AI models for detecting microsatellite instability in colorectal cancer.
We also unveil a Digital Pathology Trailblazer manifesto emphasizing the ethos and dedication of the community.
Join us to stay current with literature, advancements, and insights from the fascinating world of digital pathology.
00:00 Introduction and Announcements
00:25 Live Podcast Proposal
01:40 Welcome and Audience Interaction
03:05 Updates and Apologies
06:11 YouTube Course Announcement
07:23 Technical Difficulties and Solutions
10:00 Digital Pathology Club and Vendor Sessions
11:28 First Research Paper Discussion
17:38 Second Research Paper Discussion
20:07 ER Positive and HER2 Negative Patient Subgroup Analysis
20:59 Independent Prognostic Value of StratiPath Breast Solution
21:59 Challenges and Benefits of Image-Based Stratification
22:58 Technical Difficulties and Live Stream Interaction
24:22 Introduction to Paper Number Three: AI in Clinical Microbiology
28:07 AI in Parasitology Screening and Diagnosis
29:30 Physics-Informed AI for Retinal Assessment
33:08 AI for Microsatellite Instability Detection in Colorectal Cancer
36:42 YouTube Course Announcement and Digital Pathology Trailblazer Manifesto
42:25 Celebrating the 100th Episode of the Digital Pathology Podcast
THE ABSTRACTS WE COVERED TODAY
📄 A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.
https://pubmed.ncbi.nlm.nih.gov/39143624/
📄 Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images
https://pubmed.ncbi.nlm.nih.gov/39143539/
📄 Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis
https://pubmed.ncbi.nlm.nih.gov/39136261/
📄No longer stuck in the past: new advances in artificial intelligence and molecular assays for parasitology screening and diagnosis
https://pubmed.ncbi.nlm.nih.gov/39133581/
📄Physics-informed deep generative learning for quantitative assessment of the retina
https://pubmed.ncbi.nlm.nih.gov/39127778/
📄Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer
https://pubmed.ncbi.nlm.nih.gov/39125481/
▶️ YouTube Version of this Episode
https://www.youtube.com/live/Uwca5rzAtEA?si=Rd8r4LVM1utEKWdtSupport the show
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In this episode of DigiPath Digest, broadcasting from Poland, we delve into advances in digital pathology, including AI applications in bone marrow evaluation, classification of hematology cells, and the use of synthetic images for data augmentation. Additionally, we review a survey on pathologists' perceptions of ChatGPT and consider the feasibility of GANs for enhancing medical image analysis.
00:00 Welcome and Troubleshooting from Poland
00:21 Live Stream Challenges and Conference Details
02:21 Digital Pathology Podcast Introduction
02:51 Technical Difficulties and Audience Interaction
06:18 Exploring Digital Pathology Papers
06:43 Advances in Bone Marrow Evaluation
09:03 AI in Hematology and Pathology
12:28 Colorectal Cancer Prognostication
19:34 Pan-Cancer Xenograft Repository
25:16 ChatGPT and Pathology Survey
30:55 Synthetic Image Generation in Pathology
36:35 Upcoming Conferences and Courses
42:27 Closing Remarks and Future Plans
THE ABSTRACTS WE COVERED TODAY
📄 Advances in Bone Marrow Evaluation
https://pubmed.ncbi.nlm.nih.gov/39089749/
📄 Digital Imaging and AI Pre-classification in Hematology
https://pubmed.ncbi.nlm.nih.gov/39089746/
📄 Evaluation of CD3 and CD8 T-Cell Immunohistochemistry for Prognostication and Prediction of Benefit From Adjuvant Chemotherapy in Early-Stage Colorectal Cancer Within the QUASAR Trial
https://pubmed.ncbi.nlm.nih.gov/39083705/
📄 A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
A survey analysis of the adoption of large language models among pathologists
https://pubmed.ncbi.nlm.nih.gov/39082680/
📄 Clinical-Grade Validation of an Autofluorescence Virtual Staining System with Human Experts and a Deep Learning System for Prostate CancerSupport the show
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Exploring Foundation Models in Digital Pathology: Insights and Tools
A visual-language foundation model for computational pathology A foundation model for clinical-grade computational pathology and rare cancers detection
In today's DigiPath Digest we talk about the foundation models in pathology.
reviewing abstracts from two notable papers in Nature.
We discuss the high-level overview of these models, including Hamid Tizhoosh's insights on the vast data requirements for developing effective foundational models.
We also explore tools for literature research, comparing PubMed and Undermind.ai, and examine a useful children's book on artificial intelligence :)
The episode features audience interaction and offers updates on digital pathology trends, along with a personal anecdote on the nature of comparison based on a yoga class experience.
00:00 Introduction and Overview
00:16 Foundation Models in Pathology
00:33 Comparing Research Tools
01:03 Live Stream Interaction
01:12 Starting the Podcast
04:51 Foundation Models Explained
05:11 Research and Findings
06:34 Children's Book on AI
08:00 Deep Dive into Foundation Models
14:28 Case Studies and Examples
18:18 Discussion on Data and Models
21:00 Final Thoughts and Questions
26:24 Exploring ToxPath and Foundation Models
27:05 Introduction to Image Repositories
28:36 Using PubMed for Research
30:35 Exploring Undermined Tool
35:42 Comparing PubMed and Undermined
41:00 Final Thoughts and Recommendations
TODAY'S ABSTRACTS & RESOURCES
📄 Here are the abstracts reviewed today:▶️ Hamid Tizhoosh's lecture:
"Foundation Models and Information retrieval in Pathology"🔧 The tool we tried today
Undermind (for literature research)📕 A book we discussed :)
"ABC of Artificial Intelligence. Baby University"Support the show
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DigiPath Digest #5 is ready as audio!
We explore how AI and image datasets can accelerate medical education for both radiology and pathology.
I review comparisons between the GPT-4 vision model and convolutional neural networks for neuropathological changes in the brain.
We explore how AI can potentially reduce healthcare costs, particularly in cancer risk discrimination.
Additionally, there's a focus on AI applications in digital urine cytology for bladder cancer diagnosis.
I also share personal updates, upcoming podcast guests, and my plans for utilizing YouTube content to create an educational course.
The episode wraps up with a lively discussion on integrating AI in clinical workflows and prioritizing patient care.
TIMESTAMPS:
00:00 Introduction and Podcast Updates03:41 Guest Highlights and Personal Updates
06:33 Digital Self-Learning in Radiology
12:14 AI in Breast Cancer Risk Assessment
18:36 Comparing GPT-4 Vision and CNN in Neuropathology
21:58 Challenges in Lesion Identification
22:59 Few-Shot Learning in Neuropathology
24:42 AI in Bladder Cancer Diagnosis
29:48 Innovations in Digital Pathology
38:48 AI-Powered Clinical Workflows
44:42 Conclusion and Future Directions
Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistanceU.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancerEvaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network modelEvaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis
TODAY'S ABSTRACTS & RESOURCES:Support the show
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How can you work remotely as a doctor? Clearly some specialties, give more possibilities to do that than others and pathology is one of them.
In this episode, I talk to Dr. Todd Randolph, a pathologist living the remote pathologist lifestyle.
Dr. Randolph shares his journey into digital pathology, including his background, the evolution of his practice, and the transition to remote work.
We discuss the benefits and challenges of digital pathology, including the importance of pathology and business experience, as well as insights into AI in pathology.
Dr. Randolph also provides advice for those looking to pursue a career in digital pathology and emphasizes the importance of taking initiative and staying informed about the field.
TIMESTAMPS
00:00 Introduction to the Guest: Dr. Todd Randolph
01:05 Todd's Pathology Journey
02:04 Specialization in Pathology
03:55 Transition to Digital Pathology
05:01 Working with Lumea
11:44 Daily Life as a Remote Pathologist
13:36 Challenges and Benefits of Digital Pathology
20:37 Starting a Career in Digital Pathology
24:45 Early Days of Digital Pathology
26:17 Challenges in Digital Pathology Systems
27:46 Exploring Different Digital Pathology Systems
29:03 Impact of Digital Pathology on Work-Life Balance
32:50 Advice for Aspiring Digital Pathologists
41:11 The Role of AI in Digital Pathology
50:40 Regulatory Considerations for AI Tools
54:11 Final Thoughts and Encouragement
THIS EPISODE’S RESOURCES
Dr. Todd Randolph on LinkedInLumea’s websiteDigital Diagnostics Summit Registration LinkSupport the show
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The third episode of DigiPath Digest just took place live, but I have an audio version for the listeners.
DigiPath Digest is a review of digital pathology and IA publications abstract review that I host weekly as a live stream (on YouTube, LinkedIn, Facebook etc.)
Here is the video version if you learn more visually
Today the abstracts we discussed centered around innovations in disease detection and prognosis powered by digital pathology and AI.
TIMESTAMPS:
00:00 Welcome and Introduction00:35 DigiPath Digest Overview
01:14 Engaging with the Audience
06:09 Abstract Review: AI in Liver Fibrosis
11:21 Abstract Review: AI in Prostate Cancer
16:43 Abstract Review: AI in Glioblastoma
23:02 Abstract Review: AI in Red Blood Cell Analysis
28:38 Upcoming Events and Announcements
34:18 Closing Remarks and Future Episodes
AI-based digital pathology provides newer insightsinto lifestyle intervention-induced fibrosisregression in MASLD: An exploratory study Artificial intelligence for detection of prostate cancer
TODAY'S ABSTRACTS & RESOURCES:
in biopsies during active surveillance Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images 1 Million Segmented Red Blood Cells With 240 K Classified in 9 Shapes and 47 K Patches of 25 Manual Blood SmearsSupport the show
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