Artificial Intelligence

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AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
Santosh K.C.
Journal of Medical Systems (2020) 44:5 Article Number: 93. Date of Publication: 1 May 2020

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

Online mental health services in China during the COVID-19 outbreak
Liu S.;  Yang L.;  Zhang C.;  Xiang Y.-T.;  Liu Z.;  Hu S.;  Zhang B.
The Lancet Psychiatry (2020) 7:4 (e17-e18). Date of Publication: 1 Apr 2020

Initiation of a new infection control system for the COVID-19 outbreak
Chen X.;  Tian J.;  Li G.;  Li G.
The Lancet Infectious Diseases (2020) 20:4 (397-398). Date of Publication: 1 Apr 2020

Artificial Intelligence and Machine Learning to Fight COVID-19
Alimadadi A.;  Aryal S.;  Manandhar I.;  Munroe P.B.;  Joe B.;  Cheng X.
Physiological genomics (2020). Date of Publication: 27 Mar 2020

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Li L.;  Qin L.;  Xu Z.;  Yin Y.;  Wang X.;  Kong B.;  Bai J.;  Lu Y.;  Fang Z.;  Song Q.;  Cao K.;  Liu D.;  Wang G.;  Xu Q.;  Fang X.;  Zhang S.;  Xia J.;  Xia J.
Radiology (2020) (200905). Date of Publication: 19 Mar 2020
Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan
Husnayain A.;  Fuad A.;  Su E.C.-Y.
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases (2020). Date of Publication: 12 Mar 2020

OBJECTIVE: An emerging outbreak of COVID-19 has been detected in at least 26 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 epidemic infection in Taiwan. METHODS: We retrieved GT data for the specific locations of Taiwan nationwide and subregions using defined search terms related to coronavirus, handwashing, and face masks. RESULTS: Searches related to COVID-19 and face masks in Taiwan increased rapidly, following the announcements of Taiwan’ first imported case and reached its peak as local cases were reported. However, searches for handwashing were gradually increased in period of face masks shortage. Moreover, high to moderate correlations between Google relative search volume (RSV) and COVID-19 cases were found in Taipei (lag-3), New Taipei (lag-2), Taoyuan (lag-2), Tainan (lag-1), Taichung (lag0), and Kaohsiung (lag0). CONCLUSION: In response to the ongoing outbreak, our results demonstrated that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies to the affected population.

Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases
Ai T.;  Yang Z.;  Hou H.;  Zhan C.;  Chen C.;  Lv W.;  Tao Q.;  Sun Z.;  Xia L.
Radiology (2020) (200642). Date of Publication: 26 Feb 2020

Background Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Methods From January 6 to February 6, 2020, 1014 patients in Wuhan, China who underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCR results. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas.

Identification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are under Quarantine
Rao A.S.R.S.;  Vazquez J.A.
Infection Control and Hospital Epidemiology (2020). Date of Publication: 2020

We are proposing to use machine learning algorithms to be able to improve possible case identifications of COVID-19 more quicker when we use a mobile phone-based web survey. This will also reduce the spread in the susceptible populations.

Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak
Hurt B.;  Kligerman S.;  Hsiao A.
Journal of Thoracic Imaging (2020). Date of Publication: 2020