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人工智能在糖尿病足溃疡截肢水平方面的建议与临床医生高度相关,仅在后足截肢方面存在例外。

Artificial intelligence's suggestions for level of amputation in diabetic foot ulcers are highly correlated with those of clinicians, only with exception of hindfoot amputations.

机构信息

Department of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.

Department of Infectious Diseases and Clinical Microbiology, Ege University School of Medicine, Izmir, Turkey.

出版信息

Int Wound J. 2024 Oct;21(10):e70055. doi: 10.1111/iwj.70055.

Abstract

Diabetic foot ulcers (DFUs) are a growing public health problem, paralleling the increasing incidence of diabetes. While prevention is most effective treatment for DFUs, challenge remains on selecting the optimal treatment in cases with DFUs. Health sciences have greatly benefited from the integration of artificial intelligence (AI) applications across various fields. Regarding amputations in DFUs, both literature and clinical practice have mainly focused on strategies to prevent amputation and identify avoidable risk factor. However, there are very limited data on assistive parameters/tools that can be used to determine the level of amputation. This study investigated how well ChatGPT, with its lately released version 4o, matches the amputation level selection of an experienced team in this field. For this purpose, clinical photographs from patients who underwent amputations due to diabetic foot ulcers between May 2023 and May 2024 were submitted to the ChatGPT-4o program. The AI was tasked with recommending an appropriate amputation level based on these clinical photographs. Data from a total of 60 patients were analysed, with a median age of 64.5 years (range: 41-91). According to the Wagner Classification, 32 patients (53.3%) had grade 4 ulcers, 16 patients (26.6%) had grade 5 ulcers, 10 patients (16.6%) had grade 3 ulcers and 2 patients (3.3%) had grade 2 ulcers. A one-to-one correspondence between the AI tool's recommended amputation level and the level actually performed was observed in 50 out of 60 cases (83.3%). In the remaining 10 cases, discrepancies were noted, with the AI consistently recommending a more proximal level of amputation than what was performed. The inter-rater agreement analysis between the actual surgeries and the AI tool's recommendations yielded a Cohen's kappa coefficient of 0.808 (SD: 0.055, 95% CI: 0.701-0.916), indicating substantial agreement. Relying solely on clinical photographs, ChatGPT-4.0 demonstrates decisions that are largely consistent with those of an experienced team in determining the optimal level of amputation for DFUs, with the exception of hindfoot amputations.

摘要

糖尿病足溃疡(DFU)是一个日益严重的公共卫生问题,与糖尿病发病率的上升相平行。虽然预防是 DFU 最有效的治疗方法,但在 DFU 患者中选择最佳治疗方法仍然具有挑战性。健康科学在各个领域人工智能(AI)应用的整合中受益匪浅。关于 DFU 中的截肢,文献和临床实践主要侧重于预防截肢和确定可避免的危险因素的策略。然而,关于可以用于确定截肢水平的辅助参数/工具的相关数据非常有限。本研究调查了最近发布的 4.0 版本的 ChatGPT 在多大程度上符合该领域经验丰富的团队在截肢水平选择方面的决策。为此,将 2023 年 5 月至 2024 年 5 月期间因糖尿病足溃疡而接受截肢的患者的临床照片提交给 ChatGPT-4.0 程序。该 AI 的任务是根据这些临床照片推荐合适的截肢水平。共分析了 60 名患者的数据,中位年龄为 64.5 岁(范围:41-91 岁)。根据 Wagner 分级,32 名患者(53.3%)为 4 级溃疡,16 名患者(26.6%)为 5 级溃疡,10 名患者(16.6%)为 3 级溃疡,2 名患者(3.3%)为 2 级溃疡。在 60 例中有 50 例(83.3%)观察到 AI 工具推荐的截肢水平与实际实施的水平之间的一对一对应关系。在其余 10 例中,发现了差异,AI 始终建议更靠近近端的截肢水平。实际手术与 AI 工具建议之间的组内一致性分析产生了 Cohen's kappa 系数为 0.808(SD:0.055,95%CI:0.701-0.916),表明具有高度一致性。仅依靠临床照片,ChatGPT-4.0 在确定 DFU 的最佳截肢水平方面做出的决策与经验丰富的团队基本一致,除了后足截肢。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b275/11444738/abb633a1e920/IWJ-21-e70055-g001.jpg

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