|
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: 7/26/2024
Contributor(s): Gari Clifford, Robert Cotes, Mina Boazak, Zifan Jiang, Seyed Salman Seyedi, Ali Bahrami Rad
|
|
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: 7/19/2024
Contributor(s): Rohan Dhamdhere, Sadeer Al-Kindi, Gourav Modanwal, Anant Madabhushi
|
|
Automated Software to Quantify Breast Arterial Calcifications from Mammograms
Application
Deep learning software for the automated detection and quantification of breast arterial calcifications in screening mammograms.
Key Benefits
Optimized image segmentation accuracy with improved generalizability towards mammograms with and without BAC presence.
Robust ability to quantitate narrow or heavy BAC and/or partial depositions...
Published: 7/19/2024
Contributor(s): Hari Trivedi, Imon Banerjee, William Charles O'Neill, Aisha Urooj Khan
|
|
Artificial Neural Network for Prediction of Clinical Outcomes
Application
A machine learning model to predict the need for ventriculoperitoneal shunt after posterior fossa tumor resection.
Key Benefits
Highly accurate prediction of post-surgical complications.
Reducing healthcare costs by predicting prognoses and increasing efficiency of post-surgical treatment.
Market Summary
Neurosurgical procedures...
Published: 11/27/2023
Contributor(s): Kimberly Hoang, Ali Alawieh, David Bray
|
|
System for Assessing Health Severity and Predicting Readmissions
Application
A smartphone-based system for monitoring patients to identify and prevent severe cardiac events.
Key Benefits
A remote monitoring system that uses smartphone data to accurately predict a user’s risk for a severe cardiac event (e.g., heart attack).
Tracks the user's movement, location, and physical activity to calculate FDA-accepted...
Published: 7/26/2024
Contributor(s): Gari Clifford, Amit J. Shah, Ayse Cakmak, Erik Reinertsen
|
|
Wearable Device for the Early Detection and Monitoring of Atrial Fibrillation
Application
A wearable device that enables long-term, continuous monitoring and screening for AFib by monitoring light signals on the skin.
Key Benefits
Inexpensive and compatible with existing wearable products by leading technology companies from Apple and others.
Novel algorithms suppress irrelevant signals originating from a user's constant...
Published: 7/19/2024
Contributor(s): Shamim Nemati, Supreeth Shashikumar, Amit J. Shah, Qiao Li, Gari Clifford
|
|
Latent Variable Modeling for Developing Neuroprosthetic Devices
Application
A high-performing neural decoder that reconstructs spiking data from local field potentials to analyze neural recordings for developing neuro-prosthetic devices.
Key Benefits
Capable of augmenting local field potential to overcome perturbations in data.
Improved decoding stability with high accuracy.
Potential to lead to the development...
Published: 7/26/2024
Contributor(s): Brianna Karpowicz, Chethan Pandarinath, Yahia Ali, Marc Slutzky, Robert Flint, Bareesh Bhaduri
|
|
Automated Sleep Analysis Using Cardiovascular Signals
Application
Sleep stage classification algorithm and diagnostic platform.
Key Benefits
Real-time sleep classification using a single electrocardiogram sensor.
Eliminates the need for multiple head, eye, skin, and heart sensors with current diagnostics.
Preliminary results using human data demonstrate high accuracy in identifying different sleep...
Published: 4/24/2024
Contributor(s): Gari Clifford, Shamim Nemati, Qiao Li, Supreeth Shashikumar
|
|
Machine Learning Diagnostic for Automated Identification and Classification of Bone Marrow Cells
Application
Machine learning tool to aid in the diagnosis of hematologic disorders of bone marrow.
Key Benefits
Automated machine learning-based approach for performing bone marrow differential counts that accounts for all viable cells on the smear.
Data generated by a prototype shows the system is fast and demonstrates precision compared to manual...
Published: 11/27/2023
Contributor(s): Lee Cooper, David Gutman, David Jaye, Ahmed Aljudi, Ramraj Chandradevan
|
|
Machine Learning for Neurodegenerative Disease Diagnosis and Monitoring
Application
A machine learning classification model incorporating biomarkers for detecting Parkinson's disease.
Key Benefits
The image processing methods are simple to use, and the processing pipeline is fully automated.
The method is novel and customized to address the practical requirements of clinical and research imaging.
Market Summary
Parkinson’s...
Published: 6/21/2024
Contributor(s): Daniel Huddleston, Babak Mahmoudi
|