Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.13247 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913662251302912 |
|---|---|
| author | Fard, Reza Saadati Agu, Emmanuel Busaranuvong, Palawat Kumar, Deepak Gautam, Shefalika Tulu, Bengisu Strong, Diane |
| author_facet | Fard, Reza Saadati Agu, Emmanuel Busaranuvong, Palawat Kumar, Deepak Gautam, Shefalika Tulu, Bengisu Strong, Diane |
| contents | Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, making regular care essential to ensure healing and prevent severe outcomes like limb amputations. Many patients receive care at home from visiting nurses with varying levels of wound expertise, leading to inconsistent care. Problematic, non-healing wounds should be referred to wound specialists, but referral decisions in non-clinical settings are often erroneous, delayed, or unnecessary.
This paper introduces the Deep Multimodal Wound Assessment Tool (DM-WAT), a machine learning framework designed to assist visiting nurses in deciding whether to refer chronic wound patients. DM-WAT analyzes smartphone-captured wound images and clinical notes from Electronic Health Records (EHRs). It uses DeiT-Base-Distilled, a Vision Transformer (ViT), to extract visual features from images and DeBERTa-base to extract text features from clinical notes. DM-WAT combines visual and text features using an intermediate fusion approach. To address challenges posed by a small and imbalanced dataset, it integrates image and text augmentation with transfer learning to achieve high performance. In evaluations, DM-WAT achieved 77% with std 3% accuracy and a 70% with std 2% F1 score, outperforming prior approaches. Score-CAM and Captum interpretation algorithms provide insights into specific parts of image and text inputs that influence recommendations, enhancing interpretability and trust. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral Fard, Reza Saadati Agu, Emmanuel Busaranuvong, Palawat Kumar, Deepak Gautam, Shefalika Tulu, Bengisu Strong, Diane Machine Learning Computer Vision and Pattern Recognition Image and Video Processing Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, making regular care essential to ensure healing and prevent severe outcomes like limb amputations. Many patients receive care at home from visiting nurses with varying levels of wound expertise, leading to inconsistent care. Problematic, non-healing wounds should be referred to wound specialists, but referral decisions in non-clinical settings are often erroneous, delayed, or unnecessary. This paper introduces the Deep Multimodal Wound Assessment Tool (DM-WAT), a machine learning framework designed to assist visiting nurses in deciding whether to refer chronic wound patients. DM-WAT analyzes smartphone-captured wound images and clinical notes from Electronic Health Records (EHRs). It uses DeiT-Base-Distilled, a Vision Transformer (ViT), to extract visual features from images and DeBERTa-base to extract text features from clinical notes. DM-WAT combines visual and text features using an intermediate fusion approach. To address challenges posed by a small and imbalanced dataset, it integrates image and text augmentation with transfer learning to achieve high performance. In evaluations, DM-WAT achieved 77% with std 3% accuracy and a 70% with std 2% F1 score, outperforming prior approaches. Score-CAM and Captum interpretation algorithms provide insights into specific parts of image and text inputs that influence recommendations, enhancing interpretability and trust. |
| title | Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral |
| topic | Machine Learning Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2501.13247 |