Enhancing Diagnostics with Machine Learning

𝗡𝗲𝘄 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵! Breast cancer isn’t a single disease, it’s a complex spectrum of molecular subtypes, each demanding a tailored treatment. But the gold-standard diagnostic tools, like immunohistochemistry, can be invasive and may miss the full tumor picture. That’s why Chaima Ben Rabah, Eng-PhD, Aamenah Sattar and I asked: 𝐂𝐚𝐧 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈, 𝐮𝐬𝐢𝐧𝐠 𝐣𝐮𝐬𝐭 𝐚 𝐦𝐚𝐦𝐦𝐨𝐠𝐫𝐚𝐦 𝐚𝐧𝐝 𝐚 𝐟𝐞𝐰 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐜𝐥𝐮𝐞𝐬, 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐛𝐫𝐞𝐚𝐬𝐭 𝐜𝐚𝐧𝐜𝐞𝐫 𝐬𝐮𝐛𝐭𝐲𝐩𝐞𝐬 𝐦𝐨𝐫𝐞 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞𝐥𝐲—𝐚𝐧𝐝 𝐧𝐨𝐧-𝐢𝐧𝐯𝐚𝐬𝐢𝐯𝐞𝐥𝐲? We built a multimodal deep learning model that integrates mammography images with clinical metadata, trained on 4K images from 1.7K patients, to classify five distinct breast cancer subtypes. The results? • Our 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈 model achieved 𝟖𝟗% 𝐀𝐔𝐂 in classifying the five subtypes. • A unimodal image-only model? Just 61% AUC. • That’s a leap of over 27%—by simply letting AI listen to more than just pixels. This work shows how combining visual and clinical data through AI can unlock new levels of diagnostic precision—bringing us one step closer to personalized, non-invasive breast cancer care. 📄 Paper: https://lnkd.in/efDm46rB 💻 Code: https://lnkd.in/edB4tddF Special thanks to Ahmed Ibrahim and all the AI Innovation Lab team. Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell University Cornell Tech #AI #Innovation #MultimodalAI #DeepLearning #BreastCancer #MedicalImaging #WomenInHealth #HealthcareInnovation #DigitalHealth #MDPI #PersonalizedMedicine #HealthTech #HealthcareAI #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt

Kyle Williams, Stephen Rudin MD, Daniel Bednarek, Ammad Baig, Adnan Siddiqui, MD, PhD, Ciprian Ionita, and our team conducted a recent study that introduces a novel approach to neurovascular diagnostics that integrates artificial intelligence with physics modeling using Physics-informed Neural Networks (PINNs). This method leverages patient-specific vascular models, providing a significant improvement over traditional computational fluid dynamics (CFD). 🧠 Study Insights: ◾ Efficiency and Accuracy: Our PINNs method calculates high-resolution velocity and pressure fields in blood vessels without the manual data processing required by conventional CFD, thereby enhancing diagnostic efficiency and accuracy. ◾ Application: Successfully applied to cases such as aneurysms and carotid bifurcations, this technique supports more precise and personalized treatment planning for patients with neurovascular pathologies. By combining AI with detailed physical models, our approach streamlines the diagnostic process and enhances the accuracy of neurovascular assessments. This innovation paves the way for more advanced and patient-specific therapeutic strategies. We see the potential this brings to neurovascular healthcare and invite collaboration and discussion from peers in the field. View the abstract here: https://bit.ly/3W5aMsk #NeurovascularDiagnostics #AI #MachineLearning #HealthTech #MedicalInnovation #Neurology

A new AI model by UCLA researchers can analyze medical scans 5,000x faster than human doctors with the same accuracy. By using transfer learning from 2D medical data, SLIViT(Slice Integration by Vision Transformer) overcomes the challenge of limited 3D datasets, making it capable of analyzing complex 3D scans with incredible speed and precision. What once took 8 hours now takes just 5.8 seconds. Here’s how it works: 1. Transfer learning SLIViT is pre-trained on extensive 2D medical imaging datasets, enabling it to effectively analyze 3D scans despite the limited availability of 3D datasets. 2. Fast & accurate analysis Using a ConvNeXt backbone for feature extraction and a Vision Transformer (ViT) module for combining these features, SLIViT matches the accuracy of clinical specialists. 3. Flexibility across modalities SLIViT can analyze scans from multiple modalities, including OCT, MRI, ultrasound, and CT, making it adaptable to emerging imaging techniques and diverse clinical datasets. This AI can work with smaller datasets, making it accessible even to hospitals with limited resources. It means: -Rural clinics can offer expert-level diagnostics -Life-threatening conditions are caught earlier -Millions of patients get faster care In healthcare, speed isn’t just about efficiency - it’s about survival. And if SLIViT lives up to its claims in real-world scenarios, it could be a superpower to help save more lives, faster. Could this AI breakthrough reshape the future of medical diagnostics? #ai #innovation #healthtech

How can we use machine learning to improve clinician performance in interpreting EEG patterns and diagnosing brain injuries? This groundbreaking study that revolutionizes the healthcare industry just did it. In ICUs, critically ill patients often require precise monitoring to prevent brain injuries.  Traditional EEG methods, while crucial, can be limited by subjectivity and the availability of trained clinicians. Seizures and seizure-like EEG patterns play a critical role in neurology, significantly impacting patient outcomes. Recent advancements in machine learning have revolutionized EEG analysis, introducing more accurate and interpretable classification methods Traditionally, EEG classification heavily relied on manual review, often plagued by subjectivity due to the complexity of EEG patterns. However, a groundbreaking interpretable deep-learning model, ProtoPMed-EEG, has been developed aiming at enhancing clinician accuracy and reducing subjectivity in EEG pattern classification. How does it work?  ↳ The model integrates cutting-edge AI techniques with a unique "This EEG Looks Like That EEG" (TEEGLLTEEG) approach, allowing it to classify seizures, periodic discharges, and rhythmic activities with unprecedented accuracy. Unlike black-box models, this solution provides transparent explanations for its decisions, ensuring clinicians understand and trust its outputs. Key findings from the research include: ↬ Enhanced Accuracy: Clinicians using AI assistance demonstrated a significant improvement in classifying EEG patterns, with accuracy rising from 47% to 71%. ↬ Generalizability: Validated across multiple institutions, the model maintained high performance despite variations in dataset composition and annotator demographics. ↬ Interpretability: The unique "This EEG Looks Like That EEG" (TEEGLLTEEG) method enables an intuitive understanding of AI-generated classifications, empowering non-experts to make informed decisions confidently. A user study involving clinical practitioners highlighted the model's potential to improve diagnostic accuracy and reduce misdiagnosis risks, particularly in ICU settings lacking neurology expertise. This innovation not only aids in immediate clinical decisions but also serves as a valuable training tool for medical professionals, ensuring consistent and informed EEG pattern analysis. The study supports the Ictal-Interictal Injury Continuum hypothesis, suggesting a spectrum rather than discrete categories for EEG patterns.  This insight could revolutionize neurological diagnostics, particularly in critical care settings lacking continuous neurologist oversight. The research underscores the importance of technology that not only detects but also explains, fostering a collaborative future between AI and healthcare professionals. Follow me for more such posts. Repost this to share with your networks. #healthcare #neurology #aihealth #health #medical