Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

التفاصيل البيبلوغرافية
العنوان:	Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.
المؤلفون:	Rueckel J; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Kunz WG; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Hoppe BF; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Patzig M; Institute of Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Munich, Germany., Notohamiprodjo M; Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany.; DIE RADIOLOGIE, Munich, Germany., Meinel FG; Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany., Cyran CC; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.; DIE RADIOLOGIE, Munich, Germany., Ingrisch M; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Ricke J; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Sabel BO; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
المصدر:	Critical care medicine [Crit Care Med] 2020 Jul; Vol. 48 (7), pp. e574-e583.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0355501 Publication Model: Print Cited Medium: Internet ISSN: 1530-0293 (Electronic) Linking ISSN: 00903493 NLM ISO Abbreviation: Crit Care Med Subsets: MEDLINE
أسماء مطبوعة:	Publication: Philadelphia, PA : Lippincott Williams & Wilkins Original Publication: New York, Kolen.
مواضيع طبية MeSH:	Artificial Intelligence* , Image Interpretation, Computer-Assisted* , Radiography, Thoracic, Critical Illness/epidemiology , Lung Diseases/*diagnostic imaging, Algorithms ; Female ; Humans ; Lung Diseases/diagnosis ; Male ; Middle Aged ; Radiologists/standards ; Radiologists/statistics & numerical data ; Reproducibility of Results ; Retrospective Studies ; Supine Position ; Tomography, X-Ray Computed
مستخلص:	Objectives: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. Design: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. Patients: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. Measurements and Main Results: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. Conclusions: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting.
تواريخ الأحداث:	Date Created: 20200521 Date Completed: 20210518 Latest Revision: 20210518
رمز التحديث:	20240628
DOI:	10.1097/CCM.0000000000004397
PMID:	32433121
قاعدة البيانات:	MEDLINE

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تدمد:	1530-0293
DOI:	10.1097/CCM.0000000000004397