Table Info

Table 2.

Authors and AI methods for wound assessment and healing

Citation	Authors (Year)	Method used	Brief discussion	Limitation
[83]	Robnik-Sikonja M, et al. (2003)	Comprehensible evaluation of prognostic factors and prediction of wound healing	The authors propose a comprehensible evaluation method for prognostic factors and prediction of wound healing. The method involves analyzing various clinical and patient-related factors to develop predictive models for estimating wound healing outcomes.	Limitations may include the need for comprehensive datasets and accurate capture of relevant factors, potential challenges in quantifying and integrating diverse prognostic factors, and the dependence on accurate and consistent clinical assessments.
[80]	Cho SK, et al. (2020)	Model for predicting healing of chronic wounds within 12 weeks	The authors develop a predictive model for estimating wound healing within a 12-week timeframe. The model incorporates patient-related factors, wound characteristics, and clinical assessments to provide an objective prognosis for wound healing.	Limitations may include the need for validation in diverse patient populations, potential variations in healing outcomes for different wound types, and the dependence on accurate and consistent clinical assessments.
[74]	Howell RS, et al. (2021)	Clinical evaluation method for AI-based digital wound assessment tools	The authors describe the development of a method for the clinical evaluation of AI-based digital wound assessment tools. The method involves assessing the accuracy, reliability, and clinical utility of these tools in wound assessment.	Limitations may include the need for validation in larger and diverse patient populations, potential variations in tool performance across different wound types, and challenges in integrating the tools into clinical workflows.
[85]	Wang CW, et al. (2021)	Machine learning-assisted immune profiling for peri-implantitis patients	The authors utilize machine learning-assisted immune profiling to stratify peri-implantitis patients based on their microbial colonization and clinical outcomes. The method aims to provide personalized treatment strategies for improved management of peri-implantitis.	Limitations may include the need for comprehensive and representative datasets, potential variations in immune responses and microbial colonization across individuals, and the need for validation in larger patient populations.
[76]	Carrión H, et al. (2022)	Automatic wound detection and size estimation using deep learning	The authors employ deep learning algorithms for automatic wound detection and size estimation. The method utilizes CNNs to analyze images and accurately identify and measure wound areas.	Limitations may include the need for large and annotated datasets for training the deep learning algorithms, challenges in generalizing to different wound types, and potential difficulties in handling complex wound characteristics.
[77]	Ramachandram D, et al. (2022)	Fully automated wound tissue segmentation using deep learning on mobile devices	The authors present a fully automated method for wound tissue segmentation using deep learning algorithms deployed on mobile devices. The method aims to provide real-time and convenient wound assessment using readily available technology.	Limitations may include resource constraints of mobile devices that may limit the complexity of the deep learning models, potential challenges in generalizing to diverse wound types and characteristics, and the need for validation in larger and diverse clinical settings.
[79]	Berezo M, et al. (2022)	Machine learning for predicting chronic wound healing time	The authors develop a machine learning model for predicting the healing time of chronic wounds. The model utilizes various patient-related factors and wound characteristics to estimate the time required for wound healing.	Limitations may include the need for comprehensive and representative datasets, challenges in accurately capturing and quantifying wound characteristics, and potential variations in healing outcomes across different wound types.
[78]	Barakat-Johnson M, et al. (2022)	AI app for wound assessment and management	The authors evaluate an AI app designed to improve wound assessment and management, particularly during the COVID-19 pandemic. The app aims to provide remote wound monitoring and decision support for healthcare professionals.	Limitations may include the need for validation in larger patient populations, potential challenges in integrating the app into existing healthcare systems, and the dependence on reliable internet connectivity for remote monitoring.
[73]	Cross K, Harding K (2022)	Risk profiling using AI	The authors discuss the application of AI in risk profiling for the prevention and treatment of chronic wounds. AI techniques, such as machine learning, are utilized to develop predictive models for identifying individuals at higher risk of developing chronic wounds.	The limitations may include the need for high-quality and representative data, potential bias in the training data, and challenges in generalizability to diverse patient populations.
[81]	Ngo QC, et al. (2022)	Computerized prediction of healing for VLU	The authors propose a computerized prediction method for estimating healing outcomes of VLU. The method utilizes machine learning algorithms to analyze clinical and patient-related data and predict the likelihood of wound healing.	Limitations may include the need for comprehensive and well-curated datasets, potential challenges in accurately capturing and quantifying relevant clinical and patient-related factors, and the dependence on accurate and consistent data collection.
[63]	Anisuzzaman DM, et al. (2022)	Image-based AI	This systematic review explores the use of image-based AI in wound assessment. It provides an overview of various AI methods used in wound assessment, such as image classification, segmentation, and feature extraction.	The limitations of image-based AI in wound assessment include the need for large and diverse datasets, challenges in standardization, and potential biases in algorithm training.
[84]	Gupta R, et al. (2023)	AI-based objective prognostic model for wound healing	The authors work towards developing an AI-based objective prognostic model for quantifying wound healing. The model aims to provide accurate and reliable predictions of wound healing outcomes using various clinical and imaging data.	Limitations may include the need for large and diverse datasets, potential biases in the training data, challenges in capturing and quantifying relevant clinical and imaging factors, and the need for validation in diverse patient populations.
[82]	Tehsin S, et al. (2023)	AI for diabetic wound assessment	This mini-review discusses the application of AI in diabetic wound assessment. It provides an overview of various AI approaches used for diabetic wound analysis, including image processing, machine learning, and predictive modeling.	Limitations may include the need for large and diverse datasets, potential biases in the training data, and challenges in translating AI-based approaches into clinical practice.
[86]	Dabas M, et al. (2023)	AI in chronic wound care and management	This scoping review explores the application of AI methodologies in chronic wound care and management. It provides an overview of various AI approaches, including image analysis, machine learning, and predictive modeling, for improved wound assessment and treatment.	Limitations may include the need for validation and standardization of AI-based approaches, potential challenges in integrating AI technologies into clinical workflows, and the dependence on accurate and comprehensive data for training and validation.
[75]	Chairat S, et al. (2023)	AI-assisted assessment of wound tissue with smartphone images	The authors propose an AI-assisted method for wound tissue assessment using smartphone images. The method includes automatic color calibration and measurement calibration to enhance accuracy.	Limitations may include variations in image quality and lighting conditions captured by smartphones, potential challenges in accurately calibrating measurements, and the need for validation in diverse clinical settings.

Declarations

Author contributions

DTPL: Writing—original draft. TDP: Conceptualization, Writing—original draft. Both authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

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