Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

Abstract

Objective: Prediction of amputation wound healing is challenging due to the multifactorial nature of critical limb ischemia and lack of objective assessment tools. Up to one-third of amputations require revision to a more proximal level within 1 year. We tested a novel wound imaging system to predict amputation wound healing at initial evaluation.

Methods: Patients planned to undergo amputation due to critical limb ischemia were prospectively enrolled. Clinicians evaluated the patients in traditional fashion, and all clinical decisions for amputation level were determined by the clinician's judgement. Multispectral images of the lower extremity were obtained preoperatively using a novel wound imaging system. Clinicians were blinded to the machine analysis. A standardized wound healing assessment was performed on postoperative day 30 by physical exam to determine whether the amputation site achieved complete healing. If operative revision or higher level of amputation was required, this was undertaken based solely upon the provider's clinical judgement. A machine learning algorithm combining the multispectral imaging data with patient clinical risk factors was trained and tested using cross-validation to measure the wound imaging system's accuracy of predicting amputation wound healing.

Results: A total of 22 patients undergoing 25 amputations (10 toe, five transmetatarsal, eight below-knee, and two above-knee amputations) were enrolled. Eleven amputations (44%) were non-healing after 30 days. The machine learning algorithm had 91% sensitivity and 86% specificity for prediction of non-healing amputation sites (area under curve, 0.89).

Conclusions: This pilot study suggests that a machine learning algorithm combining multispectral wound imaging with patient clinical risk factors may improve prediction of amputation wound healing and therefore decrease the need for reoperation and incidence of delayed healing. We propose that this, in turn, may offer significant cost savings to the patient and health system in addition to decreasing length of stay for patients.

Keywords: Critical limb ischemia; Lower extremity amputation; Machine learning; Multispectral imaging.