Deep learning COVID-19 detection bias: accuracy through artificial intelligence.
Int Orthop
; 44(8): 1539-1542, 2020 08.
Article
in English
| MEDLINE | ID: covidwho-996360
ABSTRACT
BACKGROUND:
Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests.METHODS:
We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this.RESULTS:
Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans.CONCLUSIONS:
Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Pandemics
/
Betacoronavirus
/
Deep Learning
Type of study:
Prognostic study
/
Randomized controlled trials
/
Systematic review/Meta Analysis
Limits:
Adolescent
/
Adult
/
Aged
/
Child
/
Female
/
Humans
/
Male
/
Middle aged
/
Young adult
Language:
English
Journal:
Int Orthop
Year:
2020
Document Type:
Article
Affiliation country:
S00264-020-04609-7
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