使用人工智能热成像检测糖尿病患者早期足底热异常:横断面观察研究。

IF 2.6 Q2 Medicine
JMIR Diabetes Pub Date : 2025-06-13 DOI:10.2196/65209
Meshari F Alwashmi, Mustafa Alghali, AlAnoud AlMogbel, Abdullah Abdulaziz Alwabel, Abdulaziz S Alhomod, Ibrahim Almaghlouth, Mohamad-Hani Temsah, Amr Jamal
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引用次数: 0

摘要

背景:糖尿病足问题是糖尿病最严重的并发症之一。尽管医学取得了进步,但糖尿病患病率和并发症,特别是糖尿病足溃疡(DFUs)继续上升,对卫生保健构成挑战。由于时间和实际应用效率低下,传统的DFU检测方法面临可扩展性问题,导致高复发率和截肢率以及大量医疗保健成本。人体医学热成像可以显著增强疾病监测和检测,包括DFUs。目的:本研究评估了人工智能驱动的热成像在检测足底热模式方面的有效性,该模式可以区分无可见足部溃疡的成年糖尿病患者和无糖尿病的健康个体。方法:这项横断面观察性研究包括200例患者,其中100例健康患者和100例无明显足部溃疡的糖尿病患者。最初的数据是通过问卷调查收集的。参与者准备进行热成像以捕捉足底热模式。所有收集到的数据,包括热图像和问卷回答,都存储在有密码保护的计算机上,以确保数据的机密性和完整性。结果:在这项研究中,参与者被分为两组:一组是健康对照组(n=98),没有糖尿病或外周动脉疾病的诊断,循环系统正常;另一组是糖尿病患者(n=98),无论外周循环系统状况如何。温度分析显示,糖尿病组(18.1-35.6°C)比健康对照组(21.1-35.7°C)的范围更大,糖尿病组的平均温度(平均29.0°C, SD 3.0°C)明显高于健康对照组(平均28.9°C, SD 2.8°C);结论:DFUs对糖尿病患者有显著的健康风险,因此早期发现至关重要。这项研究强调了人工智能驱动的计算机视觉系统在通过区分无明显溃疡的糖尿病患者和健康个体之间的热模式来识别糖尿病足并发症早期迹象方面的潜力。研究结果表明,该技术可以改善糖尿病足护理的早期诊断和结果,尽管需要进一步的研究来充分验证其有效性。该技术检测血液供应受损的能力表明其在预防性临床策略中的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Use of AI-Powered Thermography to Detect Early Plantar Thermal Abnormalities in Patients With Diabetes: Cross-Sectional Observational Study.

Background: Diabetic foot problems are among the most debilitating complications of diabetes mellitus. Diabetes prevalence and complications, notably diabetic foot ulcers (DFUs), continue to rise, challenging health care despite advancements in medicine. Traditional DFU detection methods face scalability issues due to inefficiencies in time and practical application, leading to high recurrence and amputation rates alongside substantial health care costs. Human medical thermography could significantly enhance disease monitoring and detection, including DFUs.

Objective: This study evaluated the efficacy of artificial intelligence-powered thermography in detecting plantar thermal patterns that differentiate between adult patients with diabetes with no visible foot ulcers and healthy individuals without diabetes.

Methods: This cross-sectional observational study included 200 patients-100 healthy and 100 with diabetes without a visible foot ulcer. Initial data were gathered through a questionnaire. Participants were prepared for thermal imaging to capture plantar thermal patterns. All collected data, including thermal images and questionnaire responses, were stored on a password-protected computer to ensure confidentiality and data integrity.

Results: In this study, participants were categorized into 2 groups: a healthy control group (n=98) with no prior diabetes or peripheral artery disease diagnosis and normal circulatory findings, and a group with diabetes (n=98) comprising patients with diabetes, regardless of peripheral circulatory status. Temperature analysis indicated a wider range in the group with diabetes (18.1-35.6 °C) than in the healthy controls (21.1-35.7 °C), with the former showing significantly higher mean temperatures (mean 29.0 °C, SD 3.0 °C) than controls (mean 28.9 °C, SD 2.8 °C; P<.001). Analysis of both feet revealed significantly greater differences between feet in the group with diabetes and the controls (control: mean 0.47 °C, SD 0.43 °C; group with diabetes: mean 1.78 °C, SD 1.58 °C; P<.001; 95% CI 0.99-1.63). These results identified clinically relevant abnormalities in 10% of the cohort with diabetes, whereas no such findings were observed in the control group. We used a linear regression model to indicate that being diagnosed with diabetes is a significant predictor of abnormal temperature, while age and sex were not found to be significant predictors in this model.

Conclusions: DFUs pose a significant health risk for patients with diabetes, making early detection crucial. This study highlights the potential of an artificial intelligence-powered computer vision system in identifying early signs of diabetic foot complications by differentiating thermal patterns between patients with diabetes with no visible ulcers and healthy individuals. The findings suggest that the technology could improve early diagnosis and outcomes in diabetic foot care, although further research is needed to fully validate its effectiveness. The ability of the technology to detect compromised blood supply indicates its value in preventative clinical strategies.

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来源期刊
JMIR Diabetes
JMIR Diabetes Computer Science-Computer Science Applications
CiteScore
4.00
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35
审稿时长
16 weeks
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