![]() To systematically assess the quality of a frontal chest radiograph, use the mnemonic RIPE film. For the fullest appreciation of this discussion, the benefits of watching a technologist acquire a radiograph cannot be overstated. Here I present a simple system for evaluating the technical quality of frontal chest radiographs by examining 4 key factors. Without such understanding, the risk of making an interpretive error is increased. However, to accurately identify any pattern in a radiograph, you must first make sure that the study was obtained properly.Įvaluation of a radiograph's quality requires some understanding of the technical factors involved in the production of an x-ray image. The interpretation of chest radiographs is no different. Physicians come to recognize certain patterns as normal and others as abnormal, and the discernment of such patterns aids them in making diagnoses and other decisions. “In addition, future studies in the real-world setting will help determine the usefulness of the DLD system.”įor more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.Much medical analysis is based on pattern recognition. “Further studies in other institutions and countries are needed to ensure generalizability,” they said. The team also noted that, when used alone, the DLD outperformed the pooled observers with localization of 0.96 compared to 0.90 and AUC of 0.98 compared to 0.93.Įven with these results, the team said, additional research is needed. Using the DLD also decreased reading time from 10-to-65 seconds down to 6-to-27 seconds. In addition, localization increased from 0.90 to 0.95, and the area under the receive operating curve (AUC) jumped from 0.93 to 0.98. Specificity also improved from 89.3 percent to 96.6 percent. Not only did their per-lesion sensitivity rise from 83 percent to 89.1 percent, but their per-image sensitivity also rose from 80 percent to 89 percent. A group of six providers, including thoracic radiologists, evaluated the scans both with and without the DLD system, using a cross-over design and a washout period.Īccording to the team’s analysis, the radiologists performed better when using the DLD. “In the rigorous setting of our study, which minimized bias, the diagnostic performance of the radiologists was substantially improved with the assistance of the DLD system,” said the team led by Jinkyeong Sung from the radiology department at the University of Ulsan College of Medicine, Asan Medical Center in Seoul, South Korea.įor their study, the team collected 114 abnormal and 114 normal chest X-rays captured between January 2016 and December 2017. The team published the results of their retrospective, randomized trial on March 23 in Radiology. Instead, they said, radiologists were faster and more accurate with pinpointing abnormalities when they used CAD. Interpreting images with computer-aided detection (CAD) and, then, without the tool introduces both reading order and recall bias, said a team from South Korea. While the efficacy of artificial intelligence (AI) algorithms as a second reader has been well established in previous studies, providers included in those investigations have read scans sequentially. Simultaneous use of a deep-learning based detection (DLD) system improves a radiologist’s accuracy in identifying major abnormalities on chest X-rays, a new study has found.
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