MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system. RESULTS: The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001). CONCLUSIONS: The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.
MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system. RESULTS: The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001). CONCLUSIONS: The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.
Authors: Ngo Fung Daniel Lam; Hongfei Sun; Liming Song; Dongrong Yang; Shaohua Zhi; Ge Ren; Pak Hei Chou; Shiu Bun Nelson Wan; Man Fung Esther Wong; King Kwong Chan; Hoi Ching Hailey Tsang; Feng-Ming Spring Kong; Yì Xiáng J Wáng; Jing Qin; Lawrence Wing Chi Chan; Michael Ying; Jing Cai Journal: Quant Imaging Med Surg Date: 2022-07
Authors: Judith Becker; Josua A Decker; Christoph Römmele; Maria Kahn; Helmut Messmann; Markus Wehler; Florian Schwarz; Thomas Kroencke; Christian Scheurig-Muenkler Journal: Diagnostics (Basel) Date: 2022-06-14
Authors: Jan Rudolph; Balthasar Schachtner; Nicola Fink; Vanessa Koliogiannis; Vincent Schwarze; Sophia Goller; Lena Trappmann; Boj F Hoppe; Nabeel Mansour; Maximilian Fischer; Najib Ben Khaled; Maximilian Jörgens; Julien Dinkel; Wolfgang G Kunz; Jens Ricke; Michael Ingrisch; Bastian O Sabel; Johannes Rueckel Journal: Sci Rep Date: 2022-07-27 Impact factor: 4.996
Authors: Aileen O'Shea; Matthew D Li; Nathaniel D Mercaldo; Patricia Balthazar; Avik Som; Tristan Yeung; Marc D Succi; Brent P Little; Jayashree Kalpathy-Cramer; Susanna I Lee Journal: BJR Open Date: 2022-03-24