Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.
Biomed Eng Online
; 23(1): 12, 2024 Jan 29.
Article
en En
| MEDLINE
| ID: mdl-38287324
ABSTRACT
BACKGROUND:
The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture.RESULTS:
Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics.CONCLUSIONS:
This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Pie Diabético
/
Diabetes Mellitus
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Screening_studies
Aspecto:
Patient_preference
Límite:
Humans
Idioma:
En
Revista:
Biomed Eng Online
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Canadá