System and method for prediction of artificial intelligence model generalizability for unseen data
TS-075229 — Problem
Artificial intelligence models often perform well during development but show unpredictable drops in accuracy and reliability when deployed on data that differ from their training sets. In high‑risk settings such as clinical care, these shifts can arise from changes in hardware, protocols…
- College: College
of
Medicine
(COM)
- Inventors: Dikici, Engin; Nguyen, Xuan; Prevedello, Luciano
- Licensing Officer: Hampton, Andrew
EmitGCL: Prediction of Future Metastasis based on Graph Contrastive Learning
TS-075168 — Problem
Metastasis is the primary driver of cancer-related deaths, yet accurately predicting its onset is a major clinical challenge. Current diagnostic tools often fail to detect occult metastatic cells and metastatic precursor cells, resulting in either overdiagnosis, with unnecessary treatments,…
- College: College
of
Medicine
(COM)
- Inventors: Ma, Qin; Wang, Xiaoying
- Licensing Officer: Hampton, Andrew
Hybrid Collaborative Filtering Methods for Recommending Search Terms to Clinicians
TS-063237 — Electronic Health Records (EHR) are used in over 88% of all US medical clinics to improve care and streamline data. In addition, they enable sharing of data to multiple providers dealing with the same patient, thereby enhancing efficiency and care.
The Need
In the last decade, medical practices…
- College: College
of
Medicine
(COM)
- Inventors: Ning, Xia; Peng, Bo; Ren, Zhiyun
- Licensing Officer: Hampton, Andrew
Method for Prediction of Artificial Intelligence Model Generalizability for Unseen Data
TS-063039 — Medical-based AI systems have seen increased use in recent years across a range of applications (e.g., diagnostics, prognostics, treatment response prediction). Their widespread adoption by the medical community is still restricted, primarily due to their limited ability to realize a high degree of …
- College: College
of
Medicine
(COM)
- Inventors: Dikici, Engin; Nguyen, Xuan; Prevedello, Luciano
- Licensing Officer: Hampton, Andrew
Transforming PAH Management with PHORA: Empowering Physicians for Better Patient Care
TS-062764 — The Pulmonary Hypertension Outcomes Risk Assessment (PHORA): a PAH risk stratification tool.
Pulmonary Arterial Hypertension (PAH) demands precise risk stratification for effective patient management. Clinical decision support tools can help inform treatment decisions made by physicians and other healthcare providers, usually at the point of care.
The Need
Current methods for assistin…
- College: College
of
Medicine
(COM)
- Inventors: Benza, Ray; Lin, Shili; Mathur, Puneet
- Licensing Officer: Hampton, Andrew
AI-Enabled Retrosynthesis for Drug Development
TS-060092 — AI methods and systems for predicting reactants and their synthesis paths to support drug design and chemical synthesis.
A time-consuming and costly step in drug development is the identification of drug-like small molecules that display desired properties against a specific biomolecular target and then the synthesis of such molecules if they do not exist. Retrosynthesis is a procedure where such a desired molecule i…
- College: College
of
Medicine
(COM)
- Inventors: Ning, Xia; Chen, Ziqi
- Licensing Officer: Hampton, Andrew
Virtual image generation of immunohistochemical stained tissue sections using an artificial neural network
TS-039781 — Realistic immunohistochemical stained tissue section databases for training, certification, and machine standards.
Immunohistochemistry (IHC) is used to determine antigen distribution in a tissue and is widely used for diagnosis of cancers and other diseases. Diagnostic and research laboratories in the United States use locally devised IHC tissue slide preparation and scanning protocols to check scanner perfor…
- College: College
of
Medicine
(COM)
- Inventors: Gurcan, Metin; Lozanski, Gerard; Senaras, Caglar
- Licensing Officer: Hampton, Andrew
