Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset
Cancer Research, Statistics, and Treatment
; 5(1):19-25, 2022.
Artículo
en Inglés
| EMBASE | ID: covidwho-20239094
ABSTRACT
Background:
Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s) In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s) The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s) We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s) Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.
Artificial intelligence; assistive technology; coronavirus disease 2019; deep learning; radiology; triage; X-ray; adult; algorithm; area under the curve; article; bacterial pneumonia; deep neural network; diagnostic test accuracy study; female; ground glass opacity; human; image segmentation; influenza; lipoid pneumonia; machine learning; major clinical study; male; Pneumocystis; prediction; radiologist; receiver operating characteristic; reverse transcription polymerase chain reaction; sensitivity and specificity; thoracic vertebra; thorax radiography; training; X ray
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
EMBASE
Tipo de estudio:
Estudios diagnósticos
/
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Idioma:
Inglés
Revista:
Cancer Research, Statistics, and Treatment
Año:
2022
Tipo del documento:
Artículo
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