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Open research questions in Diabetic Foot Ulcer Assessment and Management

50 unresolved questions extracted from the limitations and future-work sections of 422 Diabetic Foot Ulcer Assessment and Management papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • This scoping review has a major limitation related to the limited number of studies available, given that digital innovation in the prevention of DFU is a relatively new and evolving field.

    Smart digital technologies for prevention of diabetic foot ulcers: A scoping review · 2026 · DOI

  • The Delphi process identified 34 critically important descriptors and 13 descriptors without consensus, which were discussed in the consensus meeting.

    Development of a core descriptor set for studies assessing interventions for diabetes-related foot ulceration · 2026 · DOI

  • To reduce the frequency of diabetic foot, we make the following recommendations: For diabetics: o Avoid wearing unsuitable footwear; o Avoid any maneuvers at foot level; o Consult a doctor immediately if you experience a problem with your lower limb. To the general population: o Participate in diabetes screenings. To the service providers: o To perform podiatric examinations at each consultation of a diabetic patient; o To refer or evacuate the at-risk foot to specialized centers in a timely manner; o To organize the hearing sessions in the departments. To the administrative and political authorities: o To establish statistics on diabetic foot across the entire level; o To provide the HGR/PANZI with a good road communication route. medRxiv preprint doi: https://doi.org/10.64898/2026.06.27.26356745; this version posted June 30, 2026. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

    Epidemiology of diabetic foot in Bukavu · 2026 · DOI

  • Further research is warranted to validate the efficacy of graded HbA1c reduction in preventing TIND and to provide clinicians with evidence-based guidance when discharging patients with permissive hyperglycaemia. Although this phenomenon has been recognised in the literature, it is relatively under-recognised compared to peripheral neuropathy and can cause significant morbidity.

    Treatment-Induced Neuropathy of Diabetes: Focus on Autonomic Neuropathy · 2026 · DOI

  • Background Diabetic foot syndrome (DFS) is characterized by chronic inflammation, thrombotic imbalance, and impaired wound healing, yet systemic molecular alterations underlying this complication remain incompletely defined.

    Dysregulation of circulating damage-associated molecular patterns in diabetic foot syndrome · 2026 · DOI

  • ABSTRACT Background Diabetic foot ulceration (DFU) and lower limb complications are highly prevalent in people with end‐stage kidney disease (ESKD), particularly those receiving dialysis; however, the overall burden and outcomes remain incompletely characterised.

    Diabetic Foot Ulceration in Dialysis‐Dependent End‐Stage Kidney Disease: A Systematic Review of Epidemiology, Clinical Outcomes and Mortality Risk · 2026 · DOI

  • prevention, especially in • Implement structured, culturally appropriate foot care education programs • Promote regular screening and early detection • Develop standardized assessment tools • Strengthen nurse-led and community-based interventions • Encourage use of telemedicine for follow-up and monitoring REFERENCES 1. Raja JM, Maturana MA, Kayali S, Khouzam A, Efeovbokhan N. Diabetic foot ulcer: A comprehensive review of pathophysiology and management modalities. 2023. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10037 283/ 2. Untari EK, Andayani TM, Yasin NM, Asdie RH. A review of patient’s knowledge and practice of diabetic foot self-care. 2024. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10917 598/ 3. Omotosho TOA, Sanyang Y, Senghore T.

    Effectiveness of Foot Care Education on Knowledge and Self Care Practices for Prevention of Diabetic Foot Ulcer Among Patients with Diabetes Mellitus: A Narrative Review of Literature · 2026 · DOI

  • In comparison, the SVM model, based on HOG feature extraction, provided a computationally efficient baseline but showed in handling complex visual patterns. The experimental results confirmed that the ensemble deep learning approach significantly outperforms the traditional SVM model in terms of accuracy and robustness. Furthermore, the integration of preprocessing techniques such as CLAHE and ROI extraction contributed to enhanced image quality and improved model performance. The pro- posed system also improves interpretability through region highlighting, making it more suitable for real-world clinical applications. Overall, this work demonstrates learning with a structured that combining ensemble deep two-stage workflow provides a reliable and effective solution for automated diabetic foot ulcer detection and severity analysis, improved supporting early diagnosis and healthcare decision-making. REFERENCES R.

    An Intelligent Comparative Study of Ensemble Deep Learning and SVM for Accurate Diabetic Foot Ulcer Detection and Severity Analysis · 2026 · DOI

  • Future research should explore whether differences in dietary and lifestyle patterns among ethnic groups in Yunnan affect the DFI pathogen spectrum through stratified studies, providing a basis for more precise localized diagnosis and treatment.

    Analysis of infection characteristics and clinical predictive indicators of different bacterial diabetic foot in Yunnan area · 2026 · DOI

  • Despite these limitations, the present cases indicate that HFSJO may warrant further investigation as a minimally invasive topical adjunct within an integrated wound care framework. Nevertheless, these observations are limited to the mon- itored clinical and laboratory parameters and do not substitute for direct quantification of systemic exposure (e.

    Curative outcomes with metal-containing TCM in diabetic foot ulcers unresponsive to standard therapy: a case series · 2026 · DOI

  • The prevention and treatment of DF is a complex systems engineering endeavor, involving multidisciplinary and multifaceted approaches. Although current research has achieved significant progress in diagnostic technologies and therapeutic methods, future development still faces numerous challenges and opportunities. 9.1 The core value of nursing management and the prospects of AI empowerment Nursing plays an indispensable role in the prevention, management, and retardation of progression for diabetic foot. Its core functions lie in high-risk population screening, personalized interventions, and continuous health selfmanagement. However, there remains a relative deficiency in objective and standardized research specifically targeting the nursing domain. AI technology offers an innovative pathway to compensate for this limitation. Recent evidence from Ju HH et al. demonstrates that nurse-led telehealth programs are highly feasible and can significantly improve patient self-care behaviors, increasing the frequency of foot self-examinations (P<.001) through structured remote education. Özgür, S. et al., utilizing ML models such as XGBoost and LightGBM, precisely identified key factors influencing foot care self-management, including age, A1c levels, and income. They leveraged AI to process gait data, formulating personalized pressure-relief nursing plans. Expanding on this, Baseman C et al. highlighted the role of computer vision (CV) and machine learning in automating the “Full Foot Exam,” enabling remote monitoring and automated classification of wound pathology with F1 scores exceeding 80%. This shift toward “Care in Place” not only improves clinical outcomes but also addresses critical health disparities; by providing accessible, AI-augmented educational tools, it is possible to reduce the disproportionate impact of DFUs on communities of color and mitigate inherent provider biases that often hinder high-quality care for marginalized populations. This approach, combined with a coordinated “hospital-community-home” management system and the potential integration of smart dressings for real-time microenvironment sensing, successfully reduced the annual foot ulcer recurrence rate to below 1%. This compellingly demonstrates that the deep integration of AI into the nursing management ecosystem is an inevitable trend for achieving precision prevention and control of diabetic foot. 9.2 AI-powered intelligent advancement of imaging technologies At present, imaging is an indispensable modality for the assessment of DFU; however, its integration with AI technology remains in a nascent stage, possessing immense potential for advancement. 9.2.1 Empowerment of foundational diagnosis based on anatomical structure At the diagnostic level, plain X-ray radiography is frequently employed as the first-line screening tool for DFU diagnosis due to its high accessibility and low cost. It can effectively identify osseous structural changes but exhibits low sensitivity for detecting gas or foreign bodies within soft tissues. CT, by virtue of its clear visualization of cortical erosion and subtle osteodystrophic changes, has become an important modality for assessing DFU-related bone destruction in emergency settings. MRI, offering the advantages of being radiation-free and high-resolution, can acutely identify bone marrow edema and abscesses. Furthermore, it aids in the differentiation of OM from CNO through multi-sequence imaging techniques, providing critical evidence for the diagnosis of complex DFU. Although X-ray, CT, and MRI are conventional imaging modalities for the clinical diagnosis of DFU, a significant research gap currently exists within the field.

    Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers · 2026 · DOI

  • The mechanism by which hydrolytic enzyme production, particularly amylase secretion by KGE15, contributes to biofilm disruption and wound healing in P. aeruginosa-infected diabetic foot ulcers requires mechanistic investigation beyond in vitro assays.

    In Vitro Discovery of Anti-bacterial and Hydrolytic Enzyme Activity of Ellisella sp.-Associated Bacteria against Pseudomonas aeruginosa in Diabetic Foot Ulcers · 2026 · DOI

  • In vivo validation of the dual therapeutic potential of KGE15 (antibacterial activity combined with amylase-mediated biofilm disruption) has not been conducted in diabetic foot ulcer wound models.

    In Vitro Discovery of Anti-bacterial and Hydrolytic Enzyme Activity of Ellisella sp.-Associated Bacteria against Pseudomonas aeruginosa in Diabetic Foot Ulcers · 2026 · DOI

  • Metabolite profiling of KGE15 and related isolates is needed to fully characterize the secondary metabolites encoded by the PKS-II biosynthetic gene cluster and their contribution to antibacterial and enzymatic activities in diabetic wound healing contexts.

    In Vitro Discovery of Anti-bacterial and Hydrolytic Enzyme Activity of Ellisella sp.-Associated Bacteria against Pseudomonas aeruginosa in Diabetic Foot Ulcers · 2026 · DOI

  • The antimicrobial compounds produced by A. soli KGE15 strain require purification and chemical characterization to determine their specific molecular structures and mechanisms of action against P. aeruginosa in diabetic foot ulcer infections.

    In Vitro Discovery of Anti-bacterial and Hydrolytic Enzyme Activity of Ellisella sp.-Associated Bacteria against Pseudomonas aeruginosa in Diabetic Foot Ulcers · 2026 · DOI

  • Looking ahead, the integration of machine learning into the care of diabetic foot ulcer (DFU) infections is expected to accelerate, driven by rapid technological innovation and evolving clinical demands. One important direction is the development of multimodal models. Instead of relying solely on wound images, future systems will incorporate diverse patient data to improve diagnostic and prognostic accuracy. A single photograph offers only a partial view of infection risk, whereas combining it with clinical variables such as body temperature, inflammatory markers, glycemic control, and past infection history could enable far more precise assessments. These models may also draw from sensor-based datasets, including thermal imaging of the foot or perfusion scans, allowing algorithms to differentiate between infectious cellulitis and benign inflammation. Early multimodal research in DFU outcome prediction supports this trajectory, demonstrating that clinical information combined with wound features enhances performance. Extending this approach to infection care opens the possibility of advanced decision-support systems that merge laboratory results or point-of-care diagnostics, like bacterial fluorescence imaging, with visual analysis to produce comprehensive infection risk scores. Achieving this vision will require large, carefully curated multimodal datasets. Another major area of progress will revolve around explainable and human-centered artificial intelligence. Machine learning tools must not only be accurate but also trustworthy and intelligible to clinicians. Current efforts are shifting away from opaque, black-box architectures toward systems capable of justifying their predictions. These may include visual cues on wound images showing the features that influenced infection probability, narrative explanations such as noting measurable increases in redness over time, or case-based comparisons with similar wounds in the training set. These elements can align AI outputs with the cognitive processes clinicians already use in decision-making. Equally critical will be design approaches tailored to real clinical workflows. Future systems may adapt dynamically to clinician corrections, reflect local patient population characteristics, and evolve alongside practice patterns. The long-term aspiration is an AI assistant that enhances professional expertise, offers transparent rationale for its recommendations, and continually refines itself based on user feedback. The widespread adoption of machine learning in DFU care will ultimately depend on rigorous validation.

    Clinical applications of machine learning for infection assessment in diabetic foot ulcers · 2026 · DOI

  • concLUSIon This study has several limitations. First, the sample size was relatively small, consisting of only 99 participants from Makassar, Indonesia. As a result, the findings may not be fully representative of other regions or populations. Additionally, the study did not include HbA1c data due to a high prevalence of anemia among the participants, which could have provided valuable insights into the relationship between glycemic control and diabetic foot complications. Furthermore, the study did not examine potential confounding variables such as socioeconomic status, access to healthcare, and specific diabetes management regimens, which could have influenced the prevalence of PAD and PN among participants. Despite the lack of HbA1c data, the study still provides significant insights into the prevalence of PAD and PN in the context of infected diabetic foot. The findings indicate that 78.8% of patients with infected diabetic foot had both PAD and PN, underscoring the critical association between these conditions and diabetic foot infections. Future studies should include larger, more diverse populations across different settings to validate these findings and explore regional variations. Additionally, further research should directly examine the role of glycemic control, potentially incorporating larger cohorts and more varied populations. This would help clarify the impact of HbA1c levels on the prevalence of PAD and PN, providing a more comprehensive understanding of diabetic foot complications. PAD and PN are key factors in the development of diabetic foot complications. However, there is limited data indicating significant differences in these conditions between individuals with and without diabetic foot, whether or not accompanied by infection. This study highlights that in cases of diabetic foot with infection, the progression of both PAD and PN is notably more severe (78.8%), contributing significantly to the condition. In contrast, the prevalence is lower in non-infected diabetic foot cases (54.5%) and even lower in those without diabetic foot (6.1%). Authors’ contributions: APH, HU, and IM drafted the manuscript. HU, SB and HR designed and concepted the study. APH and AAZ collected and analyzed and interpreted the data. IM, HR, and SB revised manuscript critically for important intellectual content. All authors participated in the final draft preparation, manuscript revision, and critical evaluation of the intellectual contents. All authors have read and approved the content of the manuscript and confirmed the accuracy or integrity of any part of the work.

    Prevalence of peripheral arterial disease and peripheral neuropathy in diabetic foot infection in Makassar, Indonesia: A cross-sectional study · 2025 · DOI

  • Out of 105 proposed data elements of managerial and clinical data in 14 groups, 90 data elements were ultimately confirmed with consensus and collective agreement according to the opinion of experts, while 12 data elements were mentioned in the open question section of the questionnaire.

    Developing a Minimum Data Set (MDS) for the Management of Diabetic Foot: Basis for Introducing Effective Indicators to the Better Management, Control and Monitoring of Diabetic Foot · 2021 · DOI

  • Current evidence suggests that PCT levels could aid clinicians in distinguishing infected from non-infected DFUs as well as in the distinction between soft tissue infection and bone involvement, but further and larger studies are warranted to confirm these findings.

    Procalcitonin as a diagnostic and prognostic marker in diabetic foot infection. A current literature review · 2017 · DOI

  • IMPLICATIONS: Future investigations will consider diabetic PD in the context of a generalized systemic wound healing deficit that manifests as PD in the face of constant pathologic wounding of the gingiva (bacterial plaque) or delayed dermal wound healing in instances of periodic traumatic wounding to other parts of the body.

    Diabetic periodontitis: possible lipid‐induced defect in tissue repair through alteration of macrophage phenotype and function · 1995 · DOI

  • Footnote: Hb: Hemoglobin; CRP: C-reactive protein; NRI: Nutritional Risk Index; NLR: Neutrophil-to-Lymphocyte Ratio…

    Nutritional Interventions and Wound Healing Outcomes in Patients With Diabetic Foot Ulcers: A Systematic Review · 2026 · DOI

  • SUMMARY Background Based on limited data, the International Working Group on the Diabetic Foot guidelines recommend antibiotics with anti-gram-negative activity, including against P.

    Global gram-negative diabetic foot infection prevalence and its associations with climate: a systematic review and meta-analysis · 2026 · DOI

  • Critical descriptors and those without consensus after the Delphi process were discussed in the consensus meeting to finalise the CDS.

    Development of a core descriptor set for studies assessing interventions for diabetes-related foot ulceration · 2026 · DOI

  • The factors influencing decision‐making for footwear use remain unclear, particularly among underserved groups such as people from low socioeconomic and South Asian backgrounds.

    Discomfort Avoidance and Desire for Normality Are Decision‐Making Drivers of Footwear Choice in Underserved Communities at Risk of Diabetic Foot Ulcer · 2026 · DOI

  • While CO2 and erbium-doped yttrium aluminum garnet lasers are well studied, diode lasers remain insufficiently investigated in ischemic DFU.

    Laser vaporization as a debridement method for diabetic foot ulcers: clinical effectiveness assessed by transcutaneous oximetry · 2026 · DOI

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50 open questions have been extracted from the limitations and future-work passages of 422 Diabetic Foot Ulcer Assessment and Management papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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