Publications by authors named "Luis Gustavo Rocha Vianna"
Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital.
Clinics (Sao Paulo)
October 2024
Article Synopsis
- - The study addresses the high mortality rates associated with hepatocellular carcinoma (HCC) and discusses the importance of CT scans in diagnosing this condition, highlighting the role of AI in medical imaging, which is limited by a lack of accessible liver imaging datasets.
- - The researchers developed HepatIA, a specialized medical imaging annotation platform that organizes data from 656 patient CT scans, using technologies like PostgreSQL, Django, and Vue.js, while also employing advanced annotation tools for accurate liver morphology analysis.
- - The resulting HepatIA database includes data from both healthy individuals and those with liver diseases, with a user-friendly interface that allows for detailed demographic searches, ultimately facilitating deep learning research on liver lesions within the medical community.
The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female.
View Article and Find Full Text PDFHepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams.
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