Type 2 diabetes mellitus is associated with impaired wound healing, which contributes substantially to patient morbidity and mortality. Glucocorticoid (stress hormone) excess is also known to delay wound repair. Optical coherence tomography (OCT) is an emerging tool for monitoring healing by 'virtual biopsy', but largely requires manual analysis, which is labour-intensive and restricts data volume processing. This limits the capability of OCT in clinical research. Using OCT data from the GC-SHEALD trial, we developed a novel machine learning algorithm for automated volumetric quantification of discrete morphological elements of wound healing (by 3mm punch biopsy) in patients with type 2 diabetes. This was able to differentiate between early / late granulation tissue, neo-epidermis and clot structural features and quantify their volumetric transition between day 2 and day 7 wounds. Using OCT, we were able to visualize differences in wound re-epithelialisation and re-modelling otherwise indistinguishable by gross wound morphology between these time points. Automated quantification of maximal early granulation tissue showed a strong correlation with corresponding (manual) GC-SHEALD data. Further, % re-epithelialisation was improved in patients treated with oral AZD4017, an inhibitor of systemic glucocorticoid-activating 11β-hydroxysteroid dehydrogenase type 1 enzyme action, with a similar trend in neo-epidermis volume. Through the combination of machine learning and OCT, we have developed a highly sensitive and reproducible method of automated volumetric quantification of wound healing. This novel approach could be further developed as a future clinical tool for the assessment of wound healing e.g. diabetic foot ulcers and pressure ulcers.

中文翻译：

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通过机器学习和光学相干断层扫描对 2 型糖尿病成人伤口愈合进行体积量化：GC-SHEALD RCT

2 型糖尿病与伤口愈合受损有关，这大大增加了患者的发病率和死亡率。已知过量的糖皮质激素（应激激素）会延迟伤口修复。光学相干断层扫描 (OCT) 是一种通过“虚拟活检”监测愈合的新兴工具，但在很大程度上需要手动分析，这是劳动密集型的并限制了数据量处理。这限制了 OCT 在临床研究中的能力。使用来自 GC-SHEALD 试验的 OCT 数据，我们开发了一种新的机器学习算法，用于自动体积量化 2 型糖尿病患者伤口愈合（通过 3 毫米穿孔活检）的离散形态元素。这能够区分早期/晚期肉芽组织，新表皮和凝块结构特征，并量化它们在第 2 天和第 7 天伤口之间的体积转变。使用 OCT，我们能够可视化伤口上皮再形成和重塑的差异，否则这些时间点之间的总体伤口形态无法区分。最大早期肉芽组织的自动量化显示出与相应（手动）GC-SHEALD 数据的强相关性。此外，口服 AZD4017（一种全身性糖皮质激素激活 11β-羟基类固醇脱氢酶 1 型酶作用的抑制剂）治疗的患者的再上皮化百分比得到改善，新表皮体积的趋势相似。通过机器学习和 OCT 的结合，我们开发了一种高度灵敏且可重复的伤口愈合自动体积量化方法。