This manuscript from our combined team led by Vladica Velickovic exploring deep learning models to improve our diagnostic and therapeutic methods to measure what we manage.
Significance Chronic wounds are associated with significant morbidity, marked loss of quality of life and considerable economic burden. Evidence-based risk prediction to guide improved wound prevention and treatment is limited by the complexity in their aetiology, clinical underreporting and a lack of studies using large high-quality datasets. Recent Advancements The objective of this review is to summarise key components and challenges in the development of personalised risk prediction tools for both prevention and management of chronic wounds, while highlighting several innovations in the development of better risk stratification. Critical issues Regression-based risk prediction approaches remain important for assessment of prognosis and risk stratification in chronic wound management. Advances in statistical computing have boosted the development of several promising machine learning and other semi-automated classification tools. These methods may be better placed to handle large number of wound healing risk factors from large datasets, potentially resulting in better risk prediction when combined with conventional methods and clinical experience and expertise. Future directions Where the number of predictors is large and heterogenous, the correlations between various risk factors complex, and very large data sets are available, then machine learning may prove a powerful adjuvant for risk-stratifying patients predisposed to chronic wounds. Conventional regression-based approaches remain important, particularly where the number of predictors is relatively small. Translating estimated risk derived from machine learning algorithms into practical prediction tools for use in clinical practice remains challenging.
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