Revolutionizing Fine Needle Aspiration Cytology: The Transformative Power of Machine Learning in Image-Guided Sample Collection and Tumor Stratification

Mitesh Gupta

doi:10.20944/preprints202409.1685.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 22 September 2024 / Approved: 22 September 2024 / Online: 23 September 2024 (12:23:07 CEST)

Fine-needle aspiration cytology (FNAC) is a pivotal diagnostic tool in oncology, utilized for evaluating suspicious lesions and stratifying tumors. The incorporation of machine learning (ML) into FNAC has revolutionized accuracy, efficiency, and diagnostic precision. This comprehensive review explores recent advances in FNAC, emphasizing the transformative role of ML in image-guided sample collection and tumor stratification. Leveraging deep learning and other ML algorithms, researchers have improved diagnostic accuracy, minimized unnecessary biopsies, and optimized treatment selection. This article highlights the transformative applications of machine learning in FNAC while addressing its current limitations and future potential.

Fine Needle Aspiration Cytology (FNAC); Machine Learning (ML); Deep Learning (DL); Image-Guided; BiopsyTumor Stratification; Diagnostic Accuracy; Artificial Intelligence (AI) in Oncology; Cytopathology; Predictive Analytics; Automated Diagnosis

Medicine and Pharmacology, Pathology and Pathobiology

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Revolutionizing Fine Needle Aspiration Cytology: The Transformative Power of Machine Learning in Image-Guided Sample Collection and Tumor Stratification

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