# A Deep Learning-based Platform for the Remote Management andAnalysis of Diabetic Foot Ulcers

> **NIH NIH R41** · ANXOMICS LLC · 2024 · $312,492

## Abstract

Project Summary:
Fifteen percent of people with diabetes suffer from diabetic foot ulcers (DFUs). Within a year of DFU diagnosis,
about 17% will need minor amputation and 5% will need a major one. Complications from non-healing infection
and ischemia worsen DFU outcomes. Enabling early recognition of infection and ischemia, and providing
appropriate therapy, would enhance healing, reduce amputations, and save substantial costs. Effective DFU
management requires infection and ischemia status evaluation at multiple time points. Access to wound
evaluation and care centers would improve outcomes, especially in high-risk, minority populations. Therefore,
there is an urgent need for non-invasive tools for detecting DFU onset of wound infection and ischemia. We
organized a new venture, Anxomics whose mission is to bring an improved, home-based diagnostic system for
DFUs to market. The company has exclusive access to a deep learning-based image analysis system to
accurately segregate wound tissue from normal skin and measure physical parameters, including size, color,
and texture. Convolution neural network-based deep-learning models trained on segmented wounds achieved
an accuracy of 79.8% in differentiating infected vs. non-infected DFUs based on images in the independent
validation set. Similarly, the ischemia phenotype-trained deep learning model achieved 94.81% accuracy.
Further testing in an independent pilot study demonstrated accuracies of 88.9% and 94.4% in infection and
ischemia identification using images captured by conventional smartphones in standard clinical settings.
Dispensing with the need for more complicated and expensive image capture technology facilitates the broad
implementation of a remote, home-based, wound management system for DFUs. The platform is being
extensively tested using DFU images acquired from people with different skin tones (collected from Emory
University Hospital in Atlanta, which serves minorities, and another hospital in north India), taken under different
lighting and with different cameras to develop robust prediction models. Our results to date confirm the potential
utility of an image-based artificial intelligence technology to offer unprecedented, rapid, and accurate predictive
diagnosis of DFU infection and ischemia status. During this Phase 1 STTR project, we propose to optimize and
validate our platform for complete analysis of the physical parameters of the wound and infection and ischemia
status by completing the following specific aims: Aim 1a: Optimize the parameters for accurate wound
segmentation and deep learning (DL) based ischemia and infection prediction on a set of prospectively collected
images. (1b) Assess the performance of deep learning-based models in predicting wound infection and ischemia
on an independent dataset. Further, we aim to develop a mobile application (DFUCare) for the management of
physical, macroscopic, and infection/ischemia data relating to wounds (Aim2).

## Key facts

- **NIH application ID:** 10931967
- **Project number:** 1R41DK139941-01A1
- **Recipient organization:** ANXOMICS LLC
- **Principal Investigator:** Manoj Bhasin
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $312,492
- **Award type:** 1
- **Project period:** 2024-06-17 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931967

## Citation

> US National Institutes of Health, RePORTER application 10931967, A Deep Learning-based Platform for the Remote Management andAnalysis of Diabetic Foot Ulcers (1R41DK139941-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10931967. Licensed CC0.

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