Publications by authors named "Cheng Cheng Goh"

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising.

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Objective: Venous leg ulcers (VLUs) are both chronic and recurrent. The treatment of such ulcers often require multiple outpatient visits and dressing changes. Several reports on the costs of treating such VLUs have been reported in the west.

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Chronic wounds are associated with significant clinical, economic and quality-of-life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU).

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Present guidelines recommend a multidisciplinary team (MDT) approach to diabetic foot ulcer (DFU) care, but relevant data from Asia are lacking. We aim to evaluate the clinical and economic outcomes of an MDT approach in a lower extremity amputation prevention programme (LEAPP) for DFU care in an Asian population. We performed a case-control study of 84 patients with DFU between January 2017 and October 2017 (retrospective control) vs 117 patients with DFU between December 2017 and July 2018 (prospective LEAPP cohort).

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Chronic venous insufficiency is a chronic disease of the venous system with a prevalence of 25% to 40% in females and 10% to 20% in males. Venous leg ulcers (VLUs) result from venous insufficiency. VLUs have a prevalence of 0.

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