Exploration of machine learning models for surgical incision healing assessment based on thermal imaging: A feasibility study

Abstract

In this study, we explored the use of thermal imaging technology combined with computer vision techniques for assessing surgical incision healing. We processed 1189 thermal images, annotated by experts to define incision boundaries and healing statuses. Using these images, we developed a machine learning model based on YOLOV8, which automates the recognition of incision areas, lesion segmentation and healing classification. The dataset was divided into training, testing and validation sets in a 7:2:1 ratio. Our results show high accuracy rates in incision location recognition, lesion segmentation and healing classification, indicating the model's effectiveness as a precise and automated diagnostic tool for surgical incision healing assessment. Conclusively, our thermal image-based machine learning model demonstrates excellent performance in wound assessment, paving the way for its clinical application in intelligent and standardized wound management.

Burden, coping and resilience among caregivers for patients with chronic obstructive pulmonary disease: An integrative review

Abstract

Aim

This study aims to synthesise quantitative and qualitative evidence to comprehensively examine the burden of family caregivers of chronic obstructive pulmonary disease patients and to understand their coping strategies and related resilience factors.

Background

Long-term chronic obstructive pulmonary disease care causes heavy psychological and physical burden to caregivers, which is related to the coping strategies used. Resilience is a protective factor originating within the individual and has become a concept related to illness, health and care.

Design

An integrative review.

Methods

Relevant literature was comprehensively searched from China Biology Medicine, China National Knowledge Infrastructure, Wan Fang, PubMed, Embase, Web of Science and Ovid databases from the establishment of the database till January 2023, and the quality of the selected articles was evaluated. Reporting was done according to a PRISMA checklist.

Findings

The burden of family caregivers with chronic obstructive pulmonary disease includes poor health, worry and fear, anticipatory loss and uncertainty, relationship tensions and disagreements, loss of identity and social isolation, lack of supportive knowledge and financial burden. Family caregivers used problem-centred coping, emotion-centred coping, avoidance coping, social support and dyadic coping with their patients to manage their burdens. The factors chronic obstructive pulmonary disease associated with a caregiver's resilience included a higher level of knowledge, social and familial support, a close relationship with patients, a caregiver's sense of responsibility, the patient's high self-efficacy, etc.

Conclusions

The findings show that caregivers of chronic obstructive pulmonary disease patients face multiple burdens, adapt through different coping styles and have different psychological consequences, while coping style and mental health status also affect the magnitude of burden.

Implications for the Profession and Patient Care

The findings informed health professionals about personalised chronic obstructive pulmonary disease home care interventions to reduce caregiver burden, effectively manage illness and maintain family intimacy.

No Patient or Public Contribution

No patients, families, service providers or members of the public were involved in this study.