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ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF SMALL, ROUND AND BLUE CELL NEOPLASMS

Grant number: 22/03123-5
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): December 01, 2022
Effective date (End): February 28, 2026
Field of knowledge:Health Sciences - Dentistry
Principal Investigator:Pablo Agustin Vargas
Grantee:Lucas Lacerda de Souza
Host Institution: Faculdade de Odontologia de Piracicaba (FOP). Universidade Estadual de Campinas (UNICAMP). Piracicaba , SP, Brazil

Abstract

Small, round and blue cell neoplasms are tumors that represent a challenge to pathologists, as they mainly share similar morphological, immunophenotypic and molecular characteristics. With the advent of more advanced computational technologies, the use of artificial intelligence techniques through digital systems and the development of algorithms for image analysis have been studied to improve agility in diagnosis. This study aims to develop a system based on Artificial Intelligence to differentiate small, round and blue cell neoplasms through a Deep Learning approach, in order to diagnose lesions with superimposed microscopic characteristics that represent challenges in the differential diagnosis of the pathology. For this, samples that present the diagnosis of the group of lesions in question will be retrospectively retrieved. The slides will be scanned, and manual annotations will be made for the validation of the images, seeking delimitation of lesional tissue and normal tissue. The images will be divided and will guide the "labels" of the patches generated after their fragmentation, being resized to a fixed size. A random division of the images will be performed using a code network according to the test that is sought to be carried out in the neural network. After the validation of neural network learning, the deep learning methodology will be used, generating a classification of the characteristics that differentiate the analyzed groups. At the end of this experiment, it is expected to obtain an algorithm that supports an assertive histopathological diagnosis for a better management of the patient.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE SOUZA, LUCAS LACERDA; FONSECA, FELIPE PAIVA; ARAUJO, ANNA LUIZA DAMACENO; LOPES, MARCIO AJUDARTE; VARGAS, PABLO AGUSTIN; KHURRAM, SYED ALI; KOWALSKI, LUIZ PAULO; DOS SANTOS, HARIM TAVARES; WARNAKULASURIYA, SAMAN; DOLEZAL, JAMES; et al. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. JOURNAL OF ORAL PATHOLOGY & MEDICINE, v. 52, n. 3, p. 9-pg., . (21/14585-7, 22/03123-5)

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