Although missed lung nodules were the most frequent missed findings at all sites, the frequency of missed findings varied substantially across the participating sites from India and the US, as well as within each country (p < 0001). The lung nodules deemed as not important likely represented calcified granulomata. reported that AI detected 13.3% of false-negative CXRs in a dataset of 4208 CXRs [].Another study by Ahn et al. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. SG, VM and VV are employees of Caring Inc. Other coauthors have no pertinent disclosures. and P.K. Examples of CXR findings missed by both the AI algorithm and in the original radiology reports: pulmonary nodule (A), consolidation (B), pleural effusion (C), pneumothorax (D) and hilar prominence (E). The ground-truth radiologists had no access to AI output at the time of interpretation. At the Indian sites, we used a natural-language-processing-based program embedded within the CARPL Platform (CARPL.AI PVT LTD., Delhi, India) to identify radiology reports of consecutive CXRs reported as normal in all sections of reports from three healthcare sites (Defense Colony Hospital, Hauz Khas Hospital and Safdarjung Hospital; all based in Delhi, India). We selected 250 consecutive CXRs from each of the 5 US sites and consecutive 450 CXRs from each of the Indian sites as the initial study size. the display of certain parts of an article in other eReaders. reported that AI detected 13.3% of false-negative CXRs in a dataset of 4208 CXRs [16]. Tam et al. (Key: NAnot applicable because there was no missed pneumothorax in patients over 65 years. All CXRs were then uploaded to a secure-server-based CARPL Annotation Platform (from the Centre for Advanced Research in Imaging, Neuroscience, and Genomics (CARING), Delhi, India) for ground-truthing. To test the hypothesis, we compared the standalone performance of an artificial intelligence (AI) algorithm for identifying missed findings on chest radiographs (CXRs) clinically reported as normal against the ground truth according to thoracic radiologists. There were variations in the performance of the algorithm across the Indian and US sites, although the differences were not statistically significant (p > 0.2). In CXRs, there is a wide range of analyzable findings, with AI algorithms from a single finding (e.g., pneumothorax, lung nodules and pneumonia) to as many as 124 radiographic findings. Several studies have reported improved sensitivity, accuracy and efficiency with the use of AI algorithms for the interpretation of CXRs [12,13]. reported a significant improvement in the detection of CXR findings with an AI algorithm compared to unaided interpretation for all six trained radiologists or trainees [17]. At the US sites, we used a radiology report database search engine, mPower (Nuance Inc., Burlington, MA, USA; Microsoft Inc., Redmond, WA, USA), to perform a similar search for CXR reports that were interpreted as normal. However, the AI algorithm had higher AUC (0.71) for detecting calcified nodules without clinical importance as compared to clinically important, non-calcified pulmonary nodules (AUC 0.55) (p = 0.006). Licensee MDPI, Basel, Switzerland. With the ground truth, there were 410 CXRs (17.1%, 410/2407), with missed findings in 342/2407 CXRs (14.2% missed finding rate). Variations in the AI algorithms performance for detecting different radiographic findings based on age group (female versus male patients). Principles and Interpretation of Chest X-rays. We obtained approval from the Human Research Committee of our Institutional Review Board (Mass General Brigham) (protocol code 2020P003950, approval date 23 December 2020). Our study outlines a compelling case for the complementary use of AI in the interpretation of CXRs but stresses the importance of careful primary interpretation of CXRs to avoid missed findingsparticularly in patients with lung nodules and consolidation. The platform was assessed in a prior research study [22]. ; writingoriginal draft preparation, P.K. 1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA, 2MGH & BWH Center for Clinical Data Science, Boston, MA 02114, USA. To avoid data sharing and maintain data privacy, all AI processing was conducted behind the institutional firewall of Massachusetts General Hospital. Thus, our final study sample size was 2407 CXRs (1262 CXRs from India; 1145 CXRs from US) (Figure 1). Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules. The lower AUCs obtained with the assessed AI algorithm for some missed findings in our study are likely related to the fact that missed findings are more likely to be subtle or difficult to detect, and therefore bring an additional level of complexity to AI performance. Easy and rapid access, familiarity, low cost and interpretation access all contribute to the widespread use of CXRs. Likewise, there are some investigations on pulmonary nodule detection by artificial intelligence in which the system was able to identify more than 99% of the nodules (false positives per image was 0.2) [27]. Kanne J.P., Thoongsuwan N., Stern E.J. Variations in the AI algorithms performance for detecting different radiographic findings based on patients stated gender (female versus male patients). We obtained the confusion matrices and area under the receiver operating characteristic (ROC) curve (AUC) from the embedded analytical and statistical functions provided within the CARPL platform. ; data curation, M.K.K. Utility of artificial intelligence tool as a prospective radiology peer reviewerDetection of unreported intracranial hemorrhage. Screening performance of the chest X-ray in adult blunt trauma evaluation: Is it effective and what does it miss? and M.T. The numbers within the parentheses represent 95% confidence intervals. The most frequent missed findings without clinical importance included subsegmental atelectasis or scarring (67/137, 62.6%), calcified lung nodules (19/137, 17.8%) and old rib fractures (11/137, 10.2%). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (. ; methodology, M.K.K. Radiographers performance in chest X-ray interpretation: The Nigerian experience. The algorithm also provided a heat map to mark the detected findings on CXRs. Tam M.D., Dyer T., Dissez G., Morgan T.N., Hughes M., Illes J., Rasalingham R., Rasalingham S. Augmenting lung cancer diagnosis on chest radiographs: Positioning artificial intelligence to improve radiologist performance. Furthermore, the AI algorithm could detect fresh, healing and old fractures with high performance (F1-scores, 0.849, 0.856 and 0.770, respectively, with p = 0.023 for each) [28]. The incremental value of AI for interpreting CXRs in our study follows the trends reported in other AI studies [23,25]. Our study limited the number of CXRs per site (250 or 400), whereas a larger number could have yielded a larger number of missed findingsespecially for findings with small numbers. The most frequent clinically important missed findings included lung nodules (158/273, 52.1%), pulmonary nodules (60/273, 19.8%) and old rib fractures (11/107, 10.3%). Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Standardized Interpretation of Chest Radiographs in Cases of Pediatric Pneumonia From the PERCH Study. ; investigation, S.R.D., B.C.B. ; validation, P.K., G.D. and R.V.G. Table 4 summarizes the performance of the AI algorithm based on thresholds determined from Youdens index. The findings and country-specific accuracies were calculated based on the vendor-suggested optimal thresholds for individual findings as well as the best performance threshold determination estimated from Youdens Index with SPSS Statistical Software (SPSS Version 32, IBM Inc., Armonk, NY, USA). Likewise, in a real-world dataset of 2972 CXRs, Jones et al. Li X., Shen L., Xie X., Huang S., Xie Z., Hong X., Yu J. Multi-resolution convolutional networks for chest X-ray radiograph-based lung nodule detection. Singh R., Kalra M.K., Nitiwarangkul C., Patti J.A., Homayounieh F., Padole A., Rao P., Putha P., Muse V.V., Sharma A., et al. 2022 Oct; 12(10): 2382. chest X-ray, missed finding, radiology, chest X-ray interpretation, AI-detected CXR findings that were not documented in the radiology reports included pulmonary nodule (, Examples of clinically important missed findings on CXRs included in our study. has an unrelated research grant from Siemens Healthineers, Riverain Tech and Coreline Inc. Four of the co-authors (A.J., P.P., B.R. Behzadi-Khormouji H., Rostami H., Salehi S., Derakhshande-Rishehri T., Masoumi M., Salemi S., Keshavarz A., Gholamrezanezhad A., Assadi M., Batouli A. and M.J.) are employees of Qure.ai. Finally, given the inter-observer variations in radiologists interpretation of CXRs, ground-truthing was performed by only two radiologists. Despite its overwhelming use, CXR interpretation is subjective and prone to wide interobserver inconsistencies based on readers knowledge and experience [5,6,7]. Deep learning in chest radiography: Detection of findings and presence of change. Each radiologist commented on the presence of any of the following CXR findings: pleural effusion, pneumothorax, consolidation, lung nodule, opacity (linear scarring or atelectasis), enlarged cardiac silhouette, mediastinal widening, hilar enlargement and rib fracture. Rudolph J., Schachtner B., Fink N., Koliogiannis V., Schwarze V., Goller S., Trappmann L., Hoppe B.F., Mansour N., Fischer M., et al. and P.K. AG, PP, BR and MT are employees of Qure.ai, who helped to organize the processing of CXRs but did not take part in case or site selection, ground-truthing or data analysis. The ground truths and AI output files were uploaded to the CARPL platform for analysis of different radiographic findings based on country, site, finding threshold (vendor-recommended and Youdens-Index-based), as well as patient gender and age. are employees of CARPL. Chest radiography (CXR) is the most performed imaging test, with substantial applications in the screening, diagnosis and monitoring of a variety of cardiothoracic disorders [1,2]. Screen captures of the AI validation platform displaying scatterplots of AI-detected and undetected CXR findings based on country (true positive (red dots), true negative (blue dots), false negative (yellow dots) and false positive (green dots)). Due to the non-interventional, retrospective nature of the study, need for written informed consent was waived. reported that their AI model led to significant changes in report in 3.1% of cases and changes in patient care for 1.4% of patients. already built in. The AI algorithms can identify patterns and perform complex computational operations more rapidly and precisely than humans [11]. Another limitation of our study is the lack of pediatric CXRs, since the assessed AI model was not trained with adequate pediatric CXRs. ; writingreview and editing, M.K.K. All 2407 frontal CXRs were exported as DICOM images and processed with an AI algorithm (Qure.ai, Mumbai, India) installed on a personal computer within our institutional firewall. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Summary of site-wise distribution of missed findings (per radiologist ground truth) with no or likely no clinical importance, which were not documented in the radiology reports. Publishers Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Related Work. Accuracy and area under the curve (AUC) of the AI algorithm based on Youdens-Index-based thresholds for different findings on CXRs. Indeed, 19% of early lung cancers that present as nodules on CXRs are missed [10]. Summary of site-wise distribution of clinically important missed findings (per radiologist ground truth) in radiology reports which were not documented in the radiology reports. To aid the interpretation of CXRs and other imaging modalities, several commercial and research computer programs have been developed and introduced to clinical practice, including those based on artificial intelligence (AI). Radiologic errors in patients with lung cancer. ; supervision, M.K.K. There are also substantial variations among radiologists, with a misinterpretation rate for CXRs as high as 30% in a prior study [8,9]. Figure 5, Figure 6 and Figure 7 display scatterplots of detected and missed CXR findings with the AI algorithm based on country (Figure 5), gender (Figure 6) and age group (Figure 7). Ueda D., Yamamoto A., Shimazaki A., Walston S.L., Matsumoto T., Izumi N., Tsukioka T., Komatsu H., Inoue H., Kabata D., et al. Users of AI models should be aware of the impact of such variations on their local CXRs. Accuracy and area under the curve (AUC) of the AI algorithm based on vendor-based thresholds for different findings on CXRs. Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1not important; 5critical importance). If successful, AI algorithms could help to improve the quality of radiology reports, enhance patient care and help avoid malpractice lawsuits from missed radiologic findings. A high frequency of missed lung nodules on CXRs has also been reported in prior studies [23]. Generating an ePub file may take a long time, please be patient. Ekpo E.U., Egbe N.O., Akpan B.E. Conceptualization, P.K. The AI algorithm is cleared for clinical use in 50 countries, including India, but did not have clearance from the US Food and Drug Administration at the time of preparation of this manuscript. and P.K. The most frequent missed findings included lung nodules (n= 177/410, 43.1%), subsegmental atelectasis or scarring (n = 67/410, 16.3%), consolidation (n = 62/410, 15.1%), enlarged cardiac silhouette (n = 35/410, 8.5%), mediastinal widening (n = 24/410, 5.8%), hilar enlargement (n = 19/410, 4.6%), rib fractures (n = 11/410, 2.7%), pleural effusions (n = 11/410, 2.7%) and pneumothorax (n = 4/410, 0.1%). Quekel L.G., Kessels A.G., Goei R., van Engelshoven J.M. Pneumothorax and mediastinal widening had the lowest AUCs for the AI algorithm, whereas highest AUCs were reported for pleural effusions, enlarged cardiac silhouette, hilar prominence and rib fractures. 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