Artificial intelligence in healthcare applications targeting cancer diagnosis-part I: data structure, preprocessing and data

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Araujo, Anna Luiza Damaceno ; Sperandio, Marcelo ; Calabrese, Giovanna ; Faria, Sarah S. ; Cardenas, Diego Armando Cardona ; Martins, Manoela Domingues ; Saldivia-Siracusa, Cristina ; Giraldo-Roldan, Daniela ; Pedroso, Caique Mariano ; Vargas, Pablo Agustin ; Lopes, Marcio Ajudarte ; Santos-Silva, Alan Roger ; Kowalski, Luiz Paulo ; Moraes, Matheus Cardoso

• Document type: Journal article
• Source: ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY; v. 140, n. 1, p. 10-pg., 2025-07-01.

Abstract

Background. Machine learning techniques hold significant potential to support the diagnosis and prognosis of diseases. However, the success of these approaches is heavily dependent on rigorous data acquisition, preprocessing and data organization. Methods. This article reviews the literature to evaluate key factors in dataset construction, focusing on data structure, preprocessing, and data organization, particularly in the context of imaging data. Results. The main issues with data construction when dealing with medical applications are noise (incorrect or irrelevant data), sparsity/ limited availability, representativeness/variability, and data imbalance (uneven class distribution).While preprocessing steps prepare the data to be suitable for the models, data organization focuses in improving data arranging to increase the model performance. Additionally, the impact of CNN complexity in processing balanced, imbalanced, and complex datasets shows that complex CNNs are not always the optimal choice for every classification problem. Conclusion. By integrating knowledge from Health Sciences and Biomedical Engineering, we aim to enhance healthcare professionals' understanding of machine learning for image analysis in Oral Medicine and Pathology. This encourages their involvement in patient recruitment and data acquisition, broadening their roles and significantly contributing to the creation of well-characterized datasets for future research and applications. (Oral Surg Oral Med Oral Pathol Oral Radiol 2025;140:79-88) (AU)

• FAPESP's process: 21/14585-7 - Artificial intelligence applied to the clinical and histopathological diagnosis of Head and Neck Cancer
• Grantee: Anna Luiza Damaceno Araujo
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 22/07276-0 - Clinicopathological and digital predictors of recurrence and malignancy of oral leukoplakia and proliferative verrucous leukoplakia: a clinical trial associated with the use of artificial intelligence
• Grantee: Caique Mariano Pedroso
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 22/13069-8 - ARTIFICIAL INTELLIGENCE FOR CLINICAL AND HISTOPATHOLOGICAL DIAGNOSIS OF INCIPIENT ORAL SQUAMOUS CELL CARCINOMA: A MULTICENTRIC INTERNATIONAL STUDY
• Grantee: Cristina Saldivia Siracusa
• Support Opportunities: Scholarships in Brazil - Doctorate