AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications

Sayed Ahmed, Ashad Kabir, Muhammad E. H. Chowdhury, Susan Nancarrow

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-review

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Abstract

Diabetes-related foot ulcers and complications are a significant concern for individuals with diabetes, leading to severe health implications such as lower-limb amputation and reduced quality of life. This chapter discusses applying AI-driven personalised offloading device prescriptions as an advanced solution for preventing such conditions. By harnessing the capabilities of artificial intelligence, this cutting-edge approach enables the prescription of offloading devices tailored to each patient’s specific requirements. This includes the patient’s preferences on offloading devices such as footwear and foot orthotics and their adaptations that suit the patient’s intention of use and lifestyle. Through a series of studies, real-world data analysis and machine learning algorithms, high-risk areas can be identified, facilitating the recommendation of precise offloading strategies, including custom orthotic insoles, shoe adaptations, or specialised footwear. By including patient-specific factors to promote adherence, proactively addressing pressure points and promoting optimal foot mechanics, these personalised offloading devices have the potential to minimise the occurrence of foot ulcers and associated complications. This chapter proposes an AI-powered Clinical Decision Support System (CDSS) to recommend personalised prescriptions of offloading devices (footwear and insoles) for patients with diabetes who are at risk of foot complications. This innovative approach signifies a transformative leap in diabetic foot care, offering promising opportunities for preventive healthcare interventions.
Original languageEnglish
Title of host publicationDiabetic Foot Ulcers - Pathogenesis, Innovative Treatments and AI Applications
PublisherIntechOpen London
Number of pages29
Publication statusPublished - 2024

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