دورية أكاديمية
Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
| العنوان: | Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs |
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| المؤلفون: | Hillis, James M., Bizzo, Bernardo C., Mercaldo, Sarah, Chin, John K., Newbury-Chaet, Isabella, Digumarthy, Subba R., Gilman, Matthew D., Muse, Victorine V., Bottrell, Georgie, Seah, Jarrel C.Y., Jones, Catherine M., Kalra, Mannudeep K., Dreyer, Keith J. |
| المصدر: | JAMA Network Open ; volume 5, issue 12, page e2247172 ; ISSN 2574-3805 |
| بيانات النشر: | American Medical Association (AMA) |
| سنة النشر: | 2022 |
| الوصف: | Importance Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. Objective To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. Design, Setting, and Participants This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. Main Outcomes and Measures The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1001/jamanetworkopen.2022.47172 |
| الإتاحة: | https://doi.org/10.1001/jamanetworkopen.2022.47172Test https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799574/hillis_2022_oi_221330_1670513479.11179.pdfTest |
| رقم الانضمام: | edsbas.136CCAB4 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1001/jamanetworkopen.2022.47172 |
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