Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation
Authors' conclusion
We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development.
Study context
- Population
- Patients with DM; 203 foot photographs (162 with ≥1 ulcer, 41 without), September 2020 to January 2021203 photographs
- Participants
- 81 patients
- Comparison
- AI system on a smartphone vs expert clinician judgement
- Outcome
- Sensitivity and specificity of the AI system for DFU detection against clinician decisions
Methodological detailsmulticenter, proof-of-concept, real-world
- Multicenter
- Proof-of-concept
- Real-world
Performance
As reported in the abstract
Automated detection of DFU vs expert clinician judgement
Sensitivity of the system
0.9157
Specificity of the system
0.8857
Specificity after merging of intersecting predictions
0.9243
Further findings
Inter- and intra-rater reliability of clinician agreement on AI DFU detection
- Krippendorff's alpha, all studies, between and within raters
- Kα > 0.8000
Original publication
Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation. Diabetes Res Clin Pract. Published 15 October 2023. doi:10.
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