Author: Cheng Jin; Weixiang Chen; Yukun Cao; Zhanwei Xu; Xin Zhang; Lei Deng; Chuansheng Zheng; Jie Zhou; Heshui Shi; Jianjiang Feng
Title: Development and Evaluation of an AI System for COVID-19 Document date: 2020_3_23
ID: k1lg8c7q_4
Snippet: As a very recent disease, we have not yet found AI studies for COVID-19 diagnosis in peer-reviewed publications, but a few reports about COVID-19 diagnosis algorithms based on chest CT in preprint form [14, 15] . Wang et al. [14] describe a COVID-19 diagnosis system with specificity of 67% and sensitivity of 74% on 216 slices extracted from CT volumes of patients (the whole dataset consists of 44 positive and 55 negative cases, but split strategy.....
Document: As a very recent disease, we have not yet found AI studies for COVID-19 diagnosis in peer-reviewed publications, but a few reports about COVID-19 diagnosis algorithms based on chest CT in preprint form [14, 15] . Wang et al. [14] describe a COVID-19 diagnosis system with specificity of 67% and sensitivity of 74% on 216 slices extracted from CT volumes of patients (the whole dataset consists of 44 positive and 55 negative cases, but split strategy of dataset is unclear). Chen et al. [15] describe a COVID-19 diagnosis system with a performance comparable to that of an expert radiologist, however the system is validated based on a quite small dataset with only 19 confirmed COVID-19 patients and only one radiologist is compared. Clearly, the development and rigorous testing of COVID-19 diagnosis algorithms remains an open topic.
Search related documents:
Co phrase search for related documents- AI study and diagnosis system: 1, 2
- confirm patient and diagnosis system: 1
- CT volume and diagnosis system: 1
- CT volume and negative case: 1
- CT volume and patient CT volume: 1, 2, 3, 4, 5, 6
- diagnosis algorithm and negative case: 1
- diagnosis system and expert radiologist: 1
- diagnosis system and recent disease: 1, 2
Co phrase search for related documents, hyperlinks ordered by date