Imaging of the Diabetic Foot

García-Diez, Ana Isabel; Batlle, Xavier; Perissinotti, Andrés; Isern-Kebschull, Jaime; Amo, Montserrat del; Soler, Juan Carlos; Bartolome, Alvaro; Bencardino, Jenny T.

doi:10.1055/s-0043-1764386

Cited by 3 publications

(1 citation statement)

References 65 publications

(236 reference statements)

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“…In cases where high-risk factors are identified, comprehensive assessments are warranted, encompassing evaluations of nerve function, circulation, and musculoskeletal integrity ( 35 ). Recent advancements in radiology and nuclear medicine offer promising avenues for simplifying the evaluation of diabetic foot complications ( 36 ). Risk-stratification systems guide the frequency and scope of subsequent screenings ( 7 ), with high-risk patients requiring more frequent evaluations and specialist referrals ( 2 ).…”

Section: Diabetic Footmentioning

confidence: 99%

The role of machine learning in advancing diabetic foot: a review

Guan,

Wang,

Niu

et al. 2024

Front. Endocrinol.

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BackgroundDiabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.ObjectiveThis review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.MethodsA meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as “Machine Learning,” “Diabetic Foot,” “Diabetic Foot Ulcers,” “Diabetic Foot Care,” “Artificial Intelligence,” and “Predictive Modeling.” This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.ResultsML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.ConclusionThe review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

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Section: Diabetic Footmentioning

confidence: 99%

The role of machine learning in advancing diabetic foot: a review

Guan,

Wang,

Niu

et al. 2024

Front. Endocrinol.

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How to do and evaluate DWI and DCE-MRI sequences for diabetic foot assessment

Martín-Noguerol,

Díaz-Angulo,

Vilanova

et al. 2023

Skeletal Radiol

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Diagnostic imaging of the diabetic foot: an EANM evidence-based guidance

Lauri,

Noriega-Álvarez,

Chakravartty

et al. 2024

Eur J Nucl Med Mol Imaging

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Purpose Consensus on the choice of the most accurate imaging strategy in diabetic foot infective and non-infective complications is still lacking. This document provides evidence-based recommendations, aiming at defining which imaging modality should be preferred in different clinical settings. Methods This working group includes 8 nuclear medicine physicians appointed by the European Association of Nuclear Medicine (EANM), 3 radiologists and 3 clinicians (one diabetologist, one podiatrist and one infectious diseases specialist) selected for their expertise in diabetic foot. The latter members formulated some clinical questions that are not completely covered by current guidelines. These questions were converted into statements and addressed through a systematic analysis of available literature by using the PICO (Population/Problem–Intervention/Indicator–Comparator–Outcome) strategy. Each consensus statement was scored for level of evidence and for recommendation grade, according to the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria. Results Nine clinical questions were formulated by clinicians and used to provide 7 evidence-based recommendations: (1) A patient with a positive probe-to-bone test, positive plain X-rays and elevated ESR should be treated for presumptive osteomyelitis (OM). (2) Advanced imaging with MRI and WBC scintigraphy, or [18F]FDG PET/CT, should be considered when it is needed to better evaluate the location, extent or severity of the infection, in order to plan more tailored treatment. (3) In a patient with suspected OM, positive PTB test but negative plain X-rays, advanced imaging with MRI or WBC scintigraphy + SPECT/CT, or with [18F]FDG PET/CT, is needed to accurately assess the extent of the infection. (4) There are no evidence-based data to definitively prefer one imaging modality over the others for detecting OM or STI in fore- mid- and hind-foot. MRI is generally the first advanced imaging modality to be performed. In case of equivocal results, radiolabelled WBC imaging or [18F]FDG PET/CT should be used to detect OM or STI. (5) MRI is the method of choice for diagnosing or excluding Charcot neuro-osteoarthropathy; [18F]FDG PET/CT can be used as an alternative. (6) If assessing whether a patient with a Charcot foot has a superimposed infection, however, WBC scintigraphy may be more accurate than [18F]FDG PET/CT in differentiating OM from Charcot arthropathy. (7) Whenever possible, microbiological or histological assessment should be performed to confirm the diagnosis. (8) Consider appealing to an additional imaging modality in a patient with persisting clinical suspicion of infection, but negative imaging. Conclusion These practical recommendations highlight, and should assist clinicians in understanding, the role of imaging in the diagnostic workup of diabetic foot complications.

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Imaging of the Diabetic Foot

Cited by 3 publications

References 65 publications

The role of machine learning in advancing diabetic foot: a review

The role of machine learning in advancing diabetic foot: a review

How to do and evaluate DWI and DCE-MRI sequences for diabetic foot assessment

Diagnostic imaging of the diabetic foot: an EANM evidence-based guidance

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