Serdar Balcı’s Post

Revolutionizing Prostate Cancer Diagnosis with AI! Attend an engaging talk by Dr. Alessandro Caputo, a pathologist passionate about digital pathology. In this Academy session, he explores the groundbreaking role of AI in improving both the diagnosis and treatment of prostate cancer. Learn how AI is transforming pathology workflows, making cancer detection faster and more precise! In this insightful session, you’ll learn: - The three main types of tissue samples pathologists work with: biopsies, transurethral resections (TURP), and prostatectomies—and how AI tools can optimize their analysis.  - How AI algorithms can improve accuracy in identifying prostate cancer while reducing the workload for pathologists.  - The challenges of distinguishing cancerous tissues from non-cancerous ones, and how AI can help avoid misdiagnosis. Whether you're a medical professional, a researcher, or simply curious about AI in healthcare, this lecture offers a deep dive into the future of prostate cancer diagnostics. Ready to explore how AI can reshape pathology? Join us at the ESDIP Academy and revolutionize your understanding of prostate cancer diagnosis with cutting-edge AI insights. Become an ESDIP member and access here: https://lnkd.in/dfTmZqB8 #ProstateCancer #AIInMedicine #PathologyInnovation #HealthcareTechnology #MedicalAI #FutureOfMedicine

Excited to share two of our groundbreaking studies at #ESTRO2024 that showcase the potential of AI and multi-omics data in transforming rectal cancer treatment, all done by the very talented Zhuoyan Shen! Part 1 - Sunday 4:30 PM, Hall 1 Introducing RADIANT: A proof-of-concept database linking multi-omics data from the ARISTOTLE trial for AI-driven predictive modelling in locally advanced rectal cancer (LARC). Key highlights: - Established a comprehensive database integrating clinical, radiotherapy, pathology, and genomic data from 589 LARC patients - Developed AI models to enrich the database, including automatic contouring of anatomical structures and analysis of tumor microenvironment - Enables hypothesis-driven and hypothesis-free analyses to personalize LARC treatment Part 2 - Monday 11:00 AM, Hall 2 A novel AI framework to quantify tumour infiltrating lymphocytes (TILs) and unravel their prognostic significance. Key findings: - Developed and validated an AI framework for automated TIL density quantification from whole slide images - High pre-treatment TIL density significantly associated with improved disease-free and overall survival outcomes - Radiotherapy-induced immune response observed in a subgroup of patients, correlating with better prognosis Join us at both sessions to learn more about these exciting advancements in #MedPhys, #Pathology, #RadOnc, and #AI! We look forward to engaging with you.

Researchers developed CHIEF, an AI model that can diagnose and predict prognosis across 19 cancer types using pathology images. The model outperformed existing AI methods in tasks like cancer detection, origin identification, and outcome prediction, achieving up to 94% accuracy across multiple datasets. https://lnkd.in/gCBAtxsU #artificialintelligence #ai #cancer

It's Colorectal Cancer Month - an opportunity to raise awareness of the condition and highlight the vital progress that researchers have been making to improve outcomes for people affected by the condition. Medical imaging has seen some fascinating developments over the past year or so, and below are two of the most exciting. 1. In January 2023, a team at Harvard Medical School combined single-cell imaging technologies and histology to create detailed, large-scale 2D and 3D maps of colorectal cancer. The maps, which rely on imaging data acquired through an imaging technique called cyclic immunofluorescence, provide doctors with new information about the structure of the cancer, as well as how it initially forms, progresses, and interacts with the rest of the body. Ultimately, the team hopes that the colorectal cancer maps can help to advance research and improve diagnosis and treatment. Learn more: https://lnkd.in/gm8satQ6 2. In April 2023, researchers at Havard and the National Cheng Kung University developed an AI tool that could accurately predict the aggressiveness of colorectal cancer from pathology images. The tool, which analyses images of tumor samples, could help doctors predict prognosis and choose the best treatment plan. The tool could be especially useful in resource-limited areas around the world where advanced pathology and tumor genetic sequencing may not be readily available. Learn more: https://lnkd.in/eARVUQFx #ColorectalCancerAwarenessMonth #MedicalResearch #Imaging

Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients #MVision #segmentation https://lnkd.in/gwHpe5WE

🌟 𝗔𝗜’𝘀 𝗥𝗼𝗹𝗲 𝗶𝗻 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗕𝗿𝗲𝗮𝘀𝘁 𝗖𝗮𝗻𝗰𝗲𝗿 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻🌟 Researchers have developed an AI that identifies women at high risk of breast cancer 2.3 times more accurately than standard screening methods. This breakthrough could revolutionize early detection, significantly increasing the chances of successful treatment. 🔍 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Early detection is crucial in the fight against breast cancer.  👩🔬 𝗧𝗵𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: The AI compares mammograms over time, detecting subtle changes that are often missed by human eyes. This method not only improves detection rates but also helps in refining prevention strategies for high-risk women. 🌍𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗘𝗾𝘂𝗶𝘁𝘆: The article also informs that they are testing the AI in women of diverse racial and ethnic backgrounds to ensure accuracy for everyone. Encourage you to read this article and share your thoughts. #BreastCancerAwareness #AI #Innovation #EarlyDetectionSavesLives #Healthcare

Histopathology image evaluation plays a crucial role in cancer diagnoses and subtype classification. However, existing artificial intelligence methods face challenges in generalizing across different digitization protocols and population samples. Introducing the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a versatile weakly supervised machine learning framework designed to extract pathology imaging features for systematic cancer assessment. CHIEF, based on extensive pretraining on a vast dataset, surpassed state-of-the-art methods by up to 36.1% in performance, demonstrating its effectiveness in addressing domain shifts. Explore the groundbreaking research behind CHIEF here: https://lnkd.in/dqqgDrUV. #Histopathology #CancerResearch #AIforHealthcare

Leveraging AI in Healthcare: A Path to Universal Improvement The journey of AI in healthcare is just beginning. Contrary to the belief that AI can’t address biases and population differences, it actually enhances outcomes across all demographics. By understanding and incorporating these intricacies, we can achieve significant improvements globally. Some populations may highlight certain intricacies due to their magnitude, but these same factors exist on smaller scales in all groups. Recognizing these can unlock low-hanging fruits for better outcomes everywhere, even in areas thought to be well-understood. I believe these AI models are ready for deployment, provided we implement safeguards and checkpoints to ensure positive outcomes. This could involve multiple reviews, including double-blind assessments, to guarantee only beneficial results. There are clear paths to ensuring only gains, with mechanisms and feedback loops that drive momentum and inspire innovation. This approach can lead to dramatic advancements, far beyond current expectations. While it’s challenging to quantify all variables, the science supports these transformative outcomes. And make no mistake about it - anyone venturing genuinely to map out the mechanisms and the myriads in motion and their downstream implications and all that - will see that my emphasizing that the real tradeoffs and considerations as functions of time are like night and day in terms of exponetials on top of exponentials greater in net impact and magnitude than the mere often benchmarked a vs b as function of time. E.g. it's not any particular acumen or brilliance that leads to one identifying that it's merely just putting on paper the mechanisms and science and my hope is that we can encourage more embracing of science where it's just science as the downstream net impacts of all that are to say the least expansive. In any event in many of the use cases i refer to there's really no theoretical way to argue that if do no harm is the litmus test - NOT pushing forward and embracing the science where it's science amounts to a clear "blow out" in terms of harm inflicted / not avoided vs what otherwise would have rolled out. Let’s embrace AI in healthcare to unlock its full potential and drive global improvements.

Read a #blog summary about this #research paper #published in Volume 15: "Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN." #Summary ⬇️ https://lnkd.in/eRPUUfyE #cancer #AI #prostatecancer

Read a #blog summary about this #research paper #published in Volume 15: "Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN." #Summary ⬇️ https://lnkd.in/eRPUUfyE #cancer #AI #prostatecancer