Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs.
Eur Respir J
; 57(5)2021 05.
Article
en En
| MEDLINE
| ID: mdl-33243843
ABSTRACT
We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy.DLAD-10 was trained with 146â717 radiographs from 108â053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day computed tomography (CT)-confirmed dataset (normalabnormal 53147) and an open-source dataset (PadChest; normalabnormal 339334) was compared with that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent and 146 nonurgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895-1.00 in the CT-confirmed dataset and 0.913-0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% (57/60)) than pooled radiologists (84.4% (152/180); p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% (17/24) versus 29.2% (7/24); p=0.006) and urgent (82.7% (258/312) versus 78.2% (244/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean±sd time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5â
s and 1840.3±1141.1 versus 2127.1±1468.2â
s, respectively; all p<0.01) and reduced the mean±sd interpretation time (20.5±22.8 versus 23.5±23.7â
s; p<0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Enfermedades Pulmonares
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Eur Respir J
Año:
2021
Tipo del documento:
Article