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Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine—A Systematic Review

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Abstract

Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.

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Authors and Affiliations

  1. Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil

    Rita Fabiane Teixeira Gomes, Manoela Domingues Martins & Vinicius Coelho Carrard

  2. Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil

    Lauren Frenzel Schuch & Manoela Domingues Martins

  3. Graduate Postgraduate Program in Dentistry, Universidade Luterana Do Brasil, Canoas, Brazil

    Emerson Ferreira Honório

  4. Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil

    Rodrigo Marques de Figueiredo, Jean Schmith & Giovanna Nunes Machado

  5. Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil

    Vinicius Coelho Carrard

  6. Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil

    Vinicius Coelho Carrard

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  1. Rita Fabiane Teixeira Gomes
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Contributions

RFTG, RMF, LFS, and VCC conceived, designed, guided, and coordinated the study and the writing. RFTG and EFH identified publication records from MEDLINE and screened the titles, abstracts, and full text of the articles. Rita identified additional articles that were not retrieved in MEDLINE. RFTG and VCC prepared the figures and tables. All the authors contributed to the writing of the article. The Abstract, Introduction, Materials and Methods, Discussion, and Conclusion sections were written jointly by RFTG and VCC. RFTG and VCC performed thorough editing of the article. All the authors revised and approved the final article.

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Correspondence to Rita Fabiane Teixeira Gomes.

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Gomes, R.F.T., Schuch, L.F., Martins, M.D. et al. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine—A Systematic Review. J Digit Imaging 36, 1060–1070 (2023). https://doi.org/10.1007/s10278-023-00775-3

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