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Introduction: Roughly 20% of surgical patients experience diabetes, a substantial risk factor that contributes to adverse outcomes after surgery, including mortality, both infectious and non-infectious complications, and extended hospital stays. Diabetic foot is a situation that may necessitate specialized anesthesia techniques. Material and Methods: Through an extensive examination of PubMed sources, we have determined the current status of the issue and have established research paths, which involve the utilization of contemporary information technologies to address the problem in individuals with diabetic foot. Results: A total of 78 papers addressing the topic of diabetic foot and anesthesia over the past decade were identified. Similarly, a total of 129 papers specifically related to machine learning and artificial intelligence technologies were discovered independently. Furthermore, regional anesthesia (RA) has been extensively documented to have advantages in promoting the restoration of function. Nevertheless, there are legitimate concerns regarding the elevated incidence of complications linked to regional anesthesia in patients with diabetes. An area of interest pertains to the length of time that the anesthetic block lasts due to neuropathic alterations in these individuals. The research project seeks to utilize machine learning techniques to evaluate the risk of RA in patients with diabetic foot and predict the duration of the blockage using clinical data such as blood glucose levels, skin condition (particularly at the foot level, assessed through photo images), and other relevant factors. This information is intended for utilization in the decision-making process, facilitated by a software application. Conclusions: The issue of diabetic foot is a present concern, and the application of contemporary technologies utilizing machine learning/artificial intelligence can enhance the decision-making process for anesthetic management in this patient population. |
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