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The Use of Artificial Intelligence on Colposcopy Images, in the Diagnosis and Staging of cervical precancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 266-270页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici Stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Iasi Department of Surgery I Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition an

关键词： Artificial Intelligence Colposcopy Images cervical precancer

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