中国全科医学2024，Vol.27Issue(36)：4582-4590,9.DOI:10.12114/j.issn.1007-9572.2024.0168
基于YOLO神经网络构建压力性损伤自动检测和分期的人工智能模型
Construction of an Artificial Intelligence-assisted System for Automatic Detection of Pressure Injury Based on the YOLO Neural Network
摘要
Abstract
Background With the aging population,the incidence of pressure injury(PI)is gradually increasing.This not only severely impacts the quality of life for patients but also increases healthcare expenditures.However,the early detection and accurate staging of PI heavily depend on specialized training.Objective To construct and validate an artificial intelligence model for the automatic detection and staging of PI aimed at enhancing the real-time nature,accuracy,and objectivity of PI diagnostics.Methods A total of 693 PI images from the electronic management system of pressure ulcers at Changshu No.1 People's Hospital were selected from January 2021 to February 2024,the images were randomly divided into a training set(551 images)and a test set(142 images),and categorized into six stages according to National Pressure Ulcer Advisory Panel(NPUAP)guidelines:StageⅠ(154 images),StageⅡ(188 images),StageⅢ(160 images),StageⅣ(82 images),deep tissue injury(57 images),and unstageable(52 images).A deep learning object detection model for PI was established using five different versions of the YOLOv8[nano(n),small(s),medium(m),large(l)and extra large(x)]neural network and transfer learning.The model evaluation metrics included accuracy,sensitivity,specificity,false positive rate,and detection speed.Finally,the model was deployed to a mobile application via the Ultralytics Hub platform,facilitating the application of the AI model in clinical practice.Results During the evaluation of a test set containing 142 PI images,the YOLOv8l version demonstrated high accuracy(0.827)and fast inference speed(68.49 fps),achieving the best balance between precision and speed among the YOLO versions.Specifically,it achieved an overall accuracy of 93.18%across all categories,a sensitivity of 76.52%,a specificity of 96.29%,and a false positive rate of 3.72%.Among the six stages of PI,the model achieved the highest accuracy for StageⅠat 95.97%.The accuracies for StageⅡ,StageⅢ,StageⅣ,deep tissue injury,and unstageable were 91.28%,91.28%,91.95%,95.30%,and 93.29%,respectively.In terms of processing speed,YOLOv8l took a total of 2.07 seconds to process 142 images,averaging 68.49 PI images per second.Conclusion The AI model based on the YOLOv8l network can quickly and accurately detect and stage PI.Deploying this model to a mobile app allows for portable use in clinical practice,demonstrating significant potential for clinical application.关键词
压力性损伤/人工智能/深度学习/YOLO/目标检测/神经网络模型/AppKey words
Pressure injury/Artificial intelligence/Deep learning/YOLO/Object detection/Neural network models/App分类
医药卫生引用本文复制引用
王珍妮,须月萍,夏开建,徐晓丹,顾丽华..基于YOLO神经网络构建压力性损伤自动检测和分期的人工智能模型[J].中国全科医学,2024,27(36):4582-4590,9.基金项目
苏州市护理学会科研项目(SZHL-B-202407) （SZHL-B-202407）
常熟市医学人工智能与大数据重点实验室能力提升项目(CYZ202301) （CYZ202301）
苏州市第二十三批科技发展计划项目(SLT2023006) （SLT2023006）
