Evidence map›Paper›PMID 39353602›Full record

ArticleInternational wound journal2024

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.

Merve Mert, Arman Vahabi, Ali Engin Daştan, Abdussamet Kuyucu, Yunus Can Ünal, Okan Tezgel, Anıl Murat Öztürk, Meltem Taşbakan, Kemal Aktuğlu

Abstract read
In one paragraph

Article in International wound journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Merve MertDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0002-4887-8467
Arman VahabiDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0002-8937-6658
Ali Engin DaştanDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0003-3637-8550
Abdussamet KuyucuDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0002-0933-6366
Yunus Can ÜnalDepartment of Orthopaedics and Traumatology, Van Educational and Research Hospital, Van, Turkey.ORCID https://orcid.org/0009-0001-6733-4211
Okan TezgelDepartment of Orthopaedics and Traumatology, Van Educational and Research Hospital, Van, Turkey.ORCID https://orcid.org/0000-0001-9497-329X
Anıl Murat ÖztürkDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0001-8674-8877
Meltem TaşbakanDepartment of Infectious Diseases and Clinical Microbiology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0002-4689-720X
Kemal AktuğluDepartment of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey.ORCID https://orcid.org/0000-0001-8058-0364

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Amputation, SurgicalArtificial IntelligenceDiabetic FootAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedchatbotChatGPTchronic wounddigital photographtechnology

Identifiers

PMID39353602
PMCPMC11444738

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.