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SMuRF: Software for Predicting Outcomes in Head and Neck Cancer
Application
Novel data learning framework for integrating radiology and pathology data for discovering prognostic biomarkers and predicting outcomes in head and neck cancer.
Key Benefits
Integrates radiology and pathology data to improve risk prediction for head and neck cancer and support more informed treatment decisions.
Can analyze multiple...
Published: 3/11/2026
Contributor(s): Bolin Song, Amaury Leroy, Anant Madabhushi
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AI-Driven Prognostic and Predictive Model for Muscle-Invasive Bladder Cancer
Application
This AI-driven prognostic and predictive model aims to predict outcomes for muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant chemo-immunotherapy, aiding in treatment decision-making and patient stratification.
Key Benefits
Harnesses nuclear morphology and architectural features for precise prognostication in MIBC...
Published: 3/2/2026
Contributor(s): Kamal Hammouda, Tilak Pathak, Anant Madabhushi, Tuomas Mirtti
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Predictive Model for Post Surgery Prostate Cancer Recurrence
Application
An artificial intelligence based prognostic model for early Biochemical recurrence (BCR) risk assessment of men post-radical prostatectomy.
Key Benefits
Identifies significant features, including tumor-infiltrating lymphocytes (TIL), in prostate cancer recurrence.
Market Summary
Prostate cancer is one of the most diagnosed cancers...
Published: 3/2/2026
Contributor(s): Sebastian Medina, Kamal Hammouda, Tilak Pathak, Anant Madabhushi, Tuomas Mirtti
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Non-invasive Diagnostic for Meningiomas
Application
A non-invasive molecular diagnostic tool that reliably predicts the tumor characteristics and risk of recurrence of meningiomas.
Key Benefits
Combines meningioma gene expression data, construction of a gene interaction network, and tumor histology data to diagnose and predict clinical outcomes.
Complete analytical handling from specimen...
Published: 1/28/2026
Contributor(s): Ali Alawieh, Youssef Ismail, Tomas Garzon-Muvdi
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AI Model for Extracting Patterns from EMG Data
Application
Aritificial neural network-based dynamical systems modeling on electromyography (EMG) data for simultaneously estimating de-noised, high-resolution muscle activation signals across multiple muscles with millisecond-timescale precision.
Key Benefits
Generates models that produce estimates that avoid trivial output solutions (e.g., replicating...
Published: 1/20/2026
Contributor(s): Lahiru Wimalasena, Chethan Pandarinath, Mohammad Reza Keshtkaran
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Automated Image Analysis of Bone Histomorphometry Using Deep Learning
Application
An automated pipeline for digital phenotyping of brightfield bone biopsy images to generate feature maps for static histomorphometry.
Key Benefits
Combines automation with deep learning models to improve tissue delineation and quantify tissue and cellular components pertinent to static histomorphometric parameters.
Incorporates Morphological...
Published: 11/7/2025
Contributor(s): Satvika Bharadwaj, Anant Madabhushi, Madhumathi Rao, Hartmut Malluche, Florence Lima
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Efficient Combinatorial Optimization
Application
Optimization heuristic for solving large-scale combinatorial problems.
Key Benefits
Efficient exploration of highly non-convex instances.
Capable of handling large-scale problems.
Reduces total computation time through massive parallelization.
Especially designed for unconstrained binary problems.
Market Summary
The combinatorial...
Published: 3/11/2026
Contributor(s): Stefan Boettcher
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Multi-Modal Sensing Platform for Diagnosing Depression and Schizophrenia
Application
A multi-sense deep learning platform and interview protocol used for detection and assessment of depression and schizophrenia.
Key Benefits
Automated and remote mental illness assessment without the need for self-reporting or clinical observation.
Improved disease tracking for patients and clinicians.
A more objective classifier and...
Published: 2/13/2026
Contributor(s): Gari Clifford, Robert Cotes, Mina Boazak, Zifan Jiang, Seyed Salman Seyedi, Ali Bahrami Rad
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STAR-Echo: Software to Predict Cardiovascular Disease in Chronic Kidney Disease Patients
Application
A novel software for prognosis of MACE in chronic kidney disease patients using spatiotemporal analysis and transformer-based radiomics models.
Key Benefits
Unique interpretable features – identifies novel features based on longitudinal changes in LVW shape (perimeter & sphericity) and texture (intensity variations) over a...
Published: 12/5/2025
Contributor(s): Rohan Dhamdhere, Sadeer Al-Kindi, Gourav Modanwal, Anant Madabhushi
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Multimodal Identification of Atrial Fibrillation Recurrence Sites
Application
Identifies areas of interest associated with atrial fibrillation recurrences.
Key Benefits
Identifies sites associated with recurrences for AF patients.
Predict AF recurrence risk in patients’ post-catheter ablation.
Development of prediction model by extracting features from the surface of interest (SOI).
Market Summary
Atrial...
Published: 1/20/2026
Contributor(s): Abhishek Midya, Anant Madabhushi, Golnoush Asaeikheybari, Mina K. Chung, Amogh Hiremath, Moore Benjamin Shoemaker, Majd A. El-Harasis, John Barnard, Rod S. Passman
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