Palavras-chave
Diagnóstico
Aprendizado profundo
Inteligência Artificial Generativa
Como Citar
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Resumo
Objetivo: Revisar a aplicação da Inteligência Artificial (IA) no diagnóstico de câncer através de exames de imagem, analisando tendências e avanços recentes na intersecção entre IA e medicina diagnóstica. Métodos: Foi realizada uma pesquisa bibliográfica em bases de dados eletrônicas, selecionando artigos científicos, revisões sistemáticas e meta-análises publicados entre 2017 e 2023. Os estudos incluíram algoritmos de IA, técnicas de aprendizado de máquina e redes neurais aplicadas a exames de imagem para detecção, classificação e diagnóstico de câncer. Resultados: A análise focou em modelos de reconhecimento de imagem para diagnóstico de câncer, priorizando métricas de sensibilidade e especificidade. Foram destacados estudos que compararam o desempenho de radiologistas com sistemas de IA mostrando que em alguns casos a IA superou os profissionais e, em outros, melhorou significativamente o desempenho dos radiologistas quando usada como assistência. Considerações finais: A IA mostrou-se uma ferramenta promissora no diagnóstico de câncer por imagem. A combinação de IA com dados clínicos pode melhorar as métricas de diagnóstico. Limitações incluem a qualidade e quantidade de imagens para treinamento, mas novas tecnologias como IA Generativa estão surgindo para superar esses desafios.
Referências
ARBYN M, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health, 2020; 8(2): 191-203.
BENYES YK, et al. A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images. Diagnostics, 2022; 12: 1838.
BOYD BJ, et al. Successful oral delivery of poorly water-soluble drugs both depends on the intraluminal behavior of drugs and of appropriate advanced drug delivery systems. European Journal of Pharmaceutical Sciences, 2019; 137: 104977.
CASCELLA M, et al. Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. J Anesth Analg Crit Care, 2023; 3(1): 19.
HOLMSTRÖM O, et al. Point-of-Care Digital Cytology with Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting. JAMA Network Open, 2021; 4(3): 211740.
HUANG HW, et al. Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers. The Journal of Dermatology, 2021; 48: 310-316.
HUSSAIN S, et al. Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. Biomed Res Int, 2022; 5164970.
JUNIOR GBV, et al. Métricas utilizadas para avaliar a eficiência de classificadores em algoritmos inteligentes. Centro de Pesquisas Avançadas em Qualidade de Vida, 2022; 14: 2(1).
KAPLAN A e HAENLEIN M. Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 2019; 62(1): 15-25.
KHOSRAVI P, et al. A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion. Journal of Magnetic Resonance Imaging, 2021; 4: 462–471.
KIM HE, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Heal, 2020; 2(3): 138–48.
KOOI T, et al. Large scale deep learningfor computer aided detection of mammographic lesions. Med Image Anal, 2017; 35: 303–312.
KUO KM, et al. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Medical Informatics and Decision Making, 2023; 23: 138.
LIN H, et al. Dual-path network with synergistic grouping loss and evidence driven risk stratification for whole slide cervical image analysis. Medical Image Analysis, 2021; 69: 101955.
LIU F, et al. Research progress of medical image recognition based on deep learning. Chin J Biomed Eng, 2018; 37(1): 86–94.
LIU J, et al. Application and research of deep learning in medical image recognition. J Qingdao Univers (Nat Sci Edit), 2018; 31(1): 69-7480.
LIU R, et al. A review of medical artificial intelligence. Global Health Journal, 2020; 4(2).
LOTTER W, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med, 2021; 27(2): 244–9.
MASOOD A, et al. Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images. Journal of Biomedics Informatics, 2018; 79: 117-128.
MATSUOKA Y, et al. Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging. International Journal of Urology, 2023; 1103-1111.
MCKINNEY SM, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020; 577(7788): 89-94.
MISHKIN D, et al. Systematic evaluation of convolution neural networks advances on the Imagenet. Computer Vision and Image Understanding, 2017; 161: 11-19.
NASSER M e YUSOF UK. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction. Diagnostics, 2023; 13: 161.
PUPPE F. Introduction to knowledge systems. Artificial Intelligence in Medicine, 1997; 9(2): 201-203.
RIVEIRA-MARTIN M, et al. Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection. Journal of Medical Imaging, 2023; 10(5): 051807.
SALIM M, et al. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncol, 2020; 6(10): 1581–8.
SANTOS MO, et al. Estimativa de Incidência de Câncer no Brasil, 2023-2025. Revista Brasileira de Cancerologia, 2023; 69(1): 213700.
SORNAPUDI S, et al. Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image. AMIA Annu Symp Proc, 2019; 820–827.
VASWANI A, et al. Attention Is All You Need. Conference on Neural Information Processing Systems. NIPS, 2017.
ZHANG X, et al. Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer. Thoracic Cancer, 2023; 1802-1811.