Academic Journal
A novel RSCA-YOLOv8s network for automatic diagnosis and graduation in pressure injury.
| Title: | A novel RSCA-YOLOv8s network for automatic diagnosis and graduation in pressure injury. |
|---|---|
| Authors: | Hu C; The Affiliated Hospital of Jiaxing University, Jiaxing, China., Sheng H; The Affiliated Hospital of Jiaxing University, Jiaxing, China., Zhang D; The Affiliated Hospital of Jiaxing University, Jiaxing, China., Zhu Z; The Affiliated Hospital of Jiaxing University, Jiaxing, China. Electronic address: zzh2025zszq@163.com., Xu D; Tongji Zhejiang College, Jiaxing, China., Ye M; The Third Affiliated Hospital of Jiaxing University, Jiaxing, China. |
| Source: | Geriatric nursing (New York, N.Y.) [Geriatr Nurs] 2026 Jul; Vol. 71, pp. 104084. Date of Electronic Publication: 2026 May 22. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Mosby-Yearbook Country of Publication: United States NLM ID: 8309633 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1528-3984 (Electronic) Linking ISSN: 01974572 NLM ISO Abbreviation: Geriatr Nurs Subsets: MEDLINE |
| Imprint Name(s): | Publication: St Louis Mo : Mosby-Yearbook Original Publication: [New York : American Journal of Nursing Co. |
| MeSH Terms: | Pressure Ulcer*/diagnosis , Detection Algorithms*, Humans |
| Abstract: | To enhance the accuracy and objectivity in PI diagnosis, this study proposes an improved PI recognition method based on YOLOv8s, which introduces a spatial and channel synergistic attention mechanism in the C2f module to enhance the feature extraction capability and embeds a multi-scale fusion module to improve the model's ability to recognize PI varying scales. This study was conducted from January 2024 to December 2024, during which 366 PI images were collected by standardized trained nurses from two tertiary Grade A hospitals in Jiaxing. The dataset was divided into a training set and a validation set in an 8:2 ratio. The improved YOLOv8s, YOLOv5, TPH-YOLO, YOLOv7, YOLOv8s, and Swin Transformer models were employed for training. Model performance was evaluated using precision, recall, F1-score, mean average precision(mAP (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest Author Chen Hu declares that she has no conflict of interest; Author Han Sheng declares that she has no conflict of interest; Author Danying Zhang declares that she has no conflict of interest; Author Zhihong Zhu declares that she has no conflict of interest; Author Dong xu declares that he has no conflict of interest; Author Min Ye declares that she has no conflict of interest. This article contains no studies with human participants performed by any authors. |
| Contributed Indexing: | Keywords: Automatic diagnosis; Graduation; Pressure injuries; YOLOv8s |
| Entry Date(s): | Date Created: 20260521 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1016/j.gerinurse.2026.104084 |
| PMID: | 42167079 |
| Database: | MEDLINE |
| ISSN: | 1528-3984 |
|---|---|
| DOI: | 10.1016/j.gerinurse.2026.104084 |