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.

Perceived quality of life and associated factors in long COVID syndrome among older Brazilians: A cross‐sectional study

Abstract

Aims and objectives

This paper aims to: (a) determine the personal, sociodemographic, clinical, behavioural, and social characteristics of older Brazilians with clinical evidence of long COVID; (b) evaluate perceived quality of life and determine its association with personal, sociodemographic, behavioural, clinical and social variables; and (c) assess significant predictors of high perceived QoL.

Background

Given the inherent vulnerabilities of the ageing process, the older people are an at-risk group for both contagion of SARS-CoV-2 and the perpetuation of residual symptoms after infection, the so-called long COVID or post-COVID syndrome.

Design

A cross-sectional survey design using the STROBE checklist.

Methods

Brazilian older people with long COVID syndrome (n = 403) completed a phone survey measuring personal, sociodemographic, behavioural, clinical, and social characteristics, and perceived Quality of Life (QoL). Data were collected from June 2021–March 2022. A multiple linear regression model was performed to identify salient variables associated with high perceived QoL.

Results

The mean age of participants was 67.7 ± 6.6 years old. The results of the multivariate regression model showed that race, home ownership, daily screen time, musculoskeletal and anxiety symptoms, and work situation were the significant predictors of QoL among COVID-19 survivors.

Conclusions

Knowledge about the persistence of physical, emotional, and social symptoms of COVID-19 can help nurses and other healthcare providers to improve the management of survivors, bringing benefits to the whole society.

Relevance to clinical practice

Given the novelty of long-COVID and its heterogeneous trajectory, interventions focusing on the repercussions and requirements unique to more vulnerable older persons should be developed and these aspects should be included in public health recommendations and policymakers' concerns.

Patient or Public Contribution

No patient or public contribution was required to design, to outcome measures or undertake this research. Patients/members of the public contributed only to the data collection.