P238: The use of artificial intelligence in 3D imaging modalities and diabetic foot disease – a systematic review

Abstract

Abstract Introduction Diabetic foot disease (DFD) is serious complication of diabetes with a multifactorial aetiology and carries a significant risk. The prevalence of DFD continues to grow globally. Artificial intelligence has been proposed in aiding early detection and risk stratification for ulceration and other major complications including sepsis, minor or major lower limb amputation and death. Aims To systematically review the literature available on the use of artificial intelligence in three-dimensional imaging modalities in diabetic foot disease. Method A literature review was conducted in accordance with PRISMA guidelines. Embase and Medline (via the Ovid interface), CINAHL (via Ebsco Host), Web of Science and Scopus databases were searched. The grey literature was also reviewed on ClinicalTrials.gov and the NIHR journals library. The medical subject headings terms “Diabetes” AND “Diabetic foot disease” AND “Artificial intelligence” and various permutations of three-dimensional imaging modalities including “Computed Tomography”, “Magnetic Resonance Imaging” and “Positron Emission Tomography” were employed in the primary search string. Articles were independently screened by two reviewers. Results 4,865 studies were identified. 102 duplicates were removed. 4,721 were excluded in the abstract screening. 42 articles underwent full text review. Existing use centers around binary use of CT based imaging in predicting risk. Conclusion The use of deep learning models is still being explored and evaluated. Current methodologies focus on wound imaging classification, plantar thermography and plantar pressures. Specialised models which evaluate 3D imaging have potential for generation of supra-human insights into extraction of novel metadata features and prediction utilising integration of multidimensional patient characteristics.