Pancreatic postoperative diabetes prediction system based on supervised deep subspace learning
US12381007B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 29, 2024 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Jul 29, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30092
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A pancreatic postoperative diabetes prediction system based on supervised deep subspace learning. A deep convolutional neural network and the MITK software are used to obtain postoperative residual pancreas area, so as to taken as the region-of-interest. Traditional image radiomics features and deep semantic features are extracted from the residual pancreas area, and a high-dimensional image feature set is constructed. Clinical factors related to diabetes, including pancreatic excision rate, fat and muscle tissue components, demographic information and living habits are extracted, and a clinical feature set is constructed. Based on a supervised deep subspace learning network, image and clinical features are represented and fused in subspace in dimensionality reduction, while a prediction model is trained to mine sensitive features highly relevant to the prediction risk of a patient suffering postoperative diabetes mellitus with a high degree of automation and discriminative accuracy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.