Endocrinology, diabetes & metabolism · 2026
Predicting Diabetic Foot Ulcer Outcomes: Machine Learning-Based Refinement of IWGDF-Approved Classifications for Outpatient Services.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mostafavi Farideh, Amini Mohammad Reza, Mehrabi Yadollah, Rezvani Melina, Toutounchi Mehrangiz, Hashemi Nazari Seyed Saeed
Аннотация
INTRODUCTION: This study aimed to validate and compare six IWGDF-approved classification systems for predicting poor prognostic outcomes in diabetic foot ulcers (DFUs), using the same sample in Iran. We also proposed modifications to enhance the performance and feasibility of these tools, in outpatient setting.
METHODS: A prospective cohort study was conducted involving 616 DFUs from 400 patients over a six-month period. We assessed the performance of six wound classification systems: Wagner, UTWCS, PEDIS/IDSA, SINBAD, WIFI, and DiaFORA. We adjusted for the key variables associated with poor outcomes that could affect the performance of these systems, employing ten machine learning techniques along with the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest methods for feature selection methods.
RESULTS: Our findings indicated that both the SINBAD and UTWCS systems exhibited comparable effectiveness in predicting outcomes, significantly surpassing other systems. Notably, modifications to the WIFI system-specifically, redefining the wound depth classification to a clearer category-yielded improved predictive capabilities, outperforming the existing systems like SINBAD and UTWCS in predicting poor prognostic outcomes in outpatient setting.
CONCLUSION: SINBAD and UTWCS systems yield the best performance in our samples. Our proposed modification on the WIFI system can enhance its applicability for outpatient services according to reported performances.
METHODS: A prospective cohort study was conducted involving 616 DFUs from 400 patients over a six-month period. We assessed the performance of six wound classification systems: Wagner, UTWCS, PEDIS/IDSA, SINBAD, WIFI, and DiaFORA. We adjusted for the key variables associated with poor outcomes that could affect the performance of these systems, employing ten machine learning techniques along with the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest methods for feature selection methods.
RESULTS: Our findings indicated that both the SINBAD and UTWCS systems exhibited comparable effectiveness in predicting outcomes, significantly surpassing other systems. Notably, modifications to the WIFI system-specifically, redefining the wound depth classification to a clearer category-yielded improved predictive capabilities, outperforming the existing systems like SINBAD and UTWCS in predicting poor prognostic outcomes in outpatient setting.
CONCLUSION: SINBAD and UTWCS systems yield the best performance in our samples. Our proposed modification on the WIFI system can enhance its applicability for outpatient services according to reported performances.