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Wang Z, Tan X, Xue Y, Xiao C, Yue K, Lin K, Wang C, Zhou Q, Zhang J. Smart diabetic foot ulcer scoring system. Sci Rep 2024; 14:11588. [PMID: 38773207 PMCID: PMC11109117 DOI: 10.1038/s41598-024-62076-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/13/2024] [Indexed: 05/23/2024] Open
Abstract
Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into "ulcer," "infection," "normal," and "gangrene" areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland-Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.
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Affiliation(s)
- Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Xinyu Tan
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Yang Xue
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Chen Xiao
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China
| | - Kejuan Yue
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Kaibin Lin
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Chong Wang
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China.
| | - Qiuhong Zhou
- Department of Clinical Nursing, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Foot Prevention and Treatment Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jianglin Zhang
- Department of Dermatology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
- Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China.
- Department of Geriatrics, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
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Zhang H, Zhao J, Farzan R, Alizadeh Otaghvar H. Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review. Int Wound J 2024; 21:e14665. [PMID: 38272811 PMCID: PMC10805538 DOI: 10.1111/iwj.14665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like 'machine learning', 'surgical' and 'wound'. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn-grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.
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Affiliation(s)
- Hui Zhang
- The Second Clinical Medical SchoolLanzhou UniversityLanzhouChina
| | - Junde Zhao
- Department of Clinical Medicine, Health Science CenterLanzhou UniversityLanzhouChina
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Hamidreza Alizadeh Otaghvar
- Associate Professor of Plastic Surgery, Trauma and Injury Research CenterIran University of Medical SciencesTehranIran
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