Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance

Abstract

We aimed to set up an Automated Radiology Alert System (ARAS) for the detection of pneumothorax in chest radiographs by a deep learning model, and to compare its efficiency and diagnostic performance with the existing Manual Radiology Alert System (MRAS) at the tertiary medical center. This study retrospectively collected 1235 chest radiographs with pneumothorax labeling from 2013 to 2019, and 337 chest radiographs with negative findings in 2019 were separated into training and validation datasets for the deep learning model of ARAS. The efficiency before and after using the model was compared in terms of alert time and report time. During parallel running of the two systems from September to October 2020, chest radiographs prospectively acquired in the emergency department with age more than 6 years served as the testing dataset for comparison of diagnostic performance. The efficiency was improved after using the model, with mean alert time improving from 8.45 min to 0.69 min and the mean report time from 2.81 days to 1.59 days. The comparison of the diagnostic performance of both systems using 3739 chest radiographs acquired during parallel running showed that the ARAS was better than the MRAS as assessed in terms of sensitivity (recall), area under receiver operating characteristic curve, and F1 score (0.837 vs. 0.256, 0.914 vs. 0.628, and 0.754 vs. 0.407, respectively), but worse in terms of positive predictive value (PPV) (precision) (0.686 vs. 1.000). This study had successfully designed a deep learning model for pneumothorax detection on chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.

Research on Lung Ultrasound Image Classification Based on Compressed Sensing

“…At present, the research of combining these traditional detection methods and deep learning and other pattern recognition methods and applying them to the diagnosis of pneumothorax has made certain progress. Kao and others [ 2 ] developed an automatic radiology alarm system, which detects pneumothorax in chest radiographs through a deep learning model, and compared with the existing automatic alarm system, the performance had been significantly improved. Cho [ 3 ] proposed detection of the location of pneumothorax in chest X‐rays using small artificial neural networks and a simple training process, which studied the chest X‐ray pneumothorax detection method based on artificial intelligence and Kim‐Monte Carlo algorithm, and the X‐ray signal is clear and easier to be detected than ultrasound signal.…”

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

“…Among the 63 studies, 56 studies identified pneumothorax on chest radiography [ 26 – 81 ], four studies on computed tomography [ 82 – 85 ], one study on ECG [ 86 ], one study used chest radiography and photography using a smartphone [ 87 ], and one study used chest radiography and tabular data [ 88 ]. Six studies developed and internally tuned DLs [ 37 , 52 , 63 , 67 , 74 , 76 ], 25 studies also internally tested their DLs [ 32 , 33 , 35 , 38 , 40 , 41 , 43 , 45 , 47 , 48 , 50 , 55 , 60 , 65 , 69 , 70 , 73 , 75 , 79 – 83 , 85 , 86 ] and 32 studies externally tested the DLs [ 26 – 31 , 34 , 36 , 39 , 42 , 44 , 46 , 49 , 51 , 53 , 54 , 56 – 59 , 61 , 62 , 64 , 66 , 68 , 71 , 72 , 77 , 78 , 84 , 87 , 88 ].…”

“…The proportion of participants with pneumothorax in each dataset also ranged widely (median (IQR) 17.2% (10.8–25.0%)). 23 studies did not describe the sex of the study participants [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 76 – 78 , 81 , 82 , 86 – 88 ] and 24 studies did not include age information [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 74 , 76 – 78 , 81 , 82 , 86 – 88 ]. Detailed dataset characteristics are shown in supplementary table S2 .…”

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis

“…As imaging examination volumes continue to rise, artificial intelligence (AI) can serve an important role in identifying high acuity conditions, such as pneumothorax, and alerting the radiologist and referring clinician. 2,3 Quicker identification may result in faster clinical intervention for the patient. Past studies have suggested that AI assisted interpretation can improve performance and efficiency for identifying pneumothorax on CXR.…”

“…There are multiple studies on computer-aided detection of pneumothorax on chest radiograph, however, their results are varied. 2,7 Understanding the value of its addition to clinical practice can serve as a valuable step in the implementation of this technology. As a result, we performed a diagnostic test accuracy systematic review and meta-analysis to assess the performance of computer-aided pneumothorax detection, as trained by DL, on CXR.…”