Rudolph J., Schachtner B., Fink N., Koliogiannis V., Schwarze V., Goller S., Trappmann L., Hoppe B.F., Mansour N., Fischer M., et al. Mittal S., Venugopal V.K., Agarwal V.K., Malhotra M., Chatha J.S., Kapur S., Gupta A., Batra V., Majumdar P., Malhotra A., et al. 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. The ePub format uses eBook readers, which have several "ease of reading" features has an unrelated research grant from Siemens Healthineers, Riverain Tech and Coreline Inc. Four of the co-authors (A.J., P.P., B.R. We document the use of an AI validation platform (CARPL) for data annotation and model output analyses of the impact of variables such as age, gender and geographic origin on AI performance. To avoid data sharing and maintain data privacy, all AI processing was conducted behind the institutional firewall of Massachusetts General Hospital. We hypothesized that an AI algorithm can reduce missed findings on CXRs. A high frequency of missed lung nodules on CXRs has also been reported in prior studies [23]. These included 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. ), Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings, Multidisciplinary Digital Publishing Institute (MDPI). ; visualization, M.K.K. Another limitation of our study is the lack of pediatric CXRs, since the assessed AI model was not trained with adequate pediatric CXRs. Despite a large number of CXRs from 2407 patients from eight sites, including community and quaternary hospitals, the included CXRs primarily originated from two large metropolitan communities. Principles and Interpretation of Chest X-rays. Related Work. Halvorsen J.G., Kunian A. Radiology in family practice: A prospective study of 14 community practices. Association of Artificial IntelligenceAided Chest Radiograph Interpretation With Reader Performance and Efficiency. Fancourt N., Deloria Knoll M., Barger-Kamate B., De Campo J., De Campo M., Diallo M., Ebruke B.E., Feikin D.R., Gleeson F., Gong W., et al. Future studies should investigate if the use of multiple AI algorithms can further reduce missed finding rates and thereby improve the quality and content of CXR reports. However, both radiologists had multiple years of experience as practicing thoracic radiologists and fellowship training in thoracic imaging. ; project administration, M.K.K. Examples of clinically important missed findings on CXRs included in our study. and S.R.D. Users of AI models should be aware of the impact of such variations on their local CXRs. and P.K. Validation of AI models across diverse datasets is critical for establishing their generalizability. 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. Likewise, in a real-world dataset of 2972 CXRs, Jones et al. Another co-author (M.K.K.) Although the assessed AI algorithm was not perfect, it successfully detected a substantial number of findings missed by radiologists at eight different sites. In addition, the platform provided an interactive scatter plot to identify the distribution of false-positive and false-negative findings. 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. Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. The projectional nature of CXRs, the subtlety of radiographic findings and the subjective nature of radiographic interpretation pose similar problems to both AI models and human interpreters. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Quekel L.G., Kessels A.G., Goei R., van Engelshoven J.M. ; formal analysis, P.K. and P.K. Standardized Interpretation of Chest Radiographs in Cases of Pediatric Pneumonia From the PERCH Study. Kerr J.K., Jelinek R. Impact of technology in health care and health administration: Hospitals and alternative care delivery systems. 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. The numbers within the parentheses represent 95% confidence intervals. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (. 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. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: A retrospective clinical validation study. Disagreements between the two radiologists were resolved in a consensus, joint review to establish the final ground truth. Variations in the AI algorithms performance for detecting different radiographic findings based on age group (female versus male patients). To assess the generalizability of AI results, the validation platform helped to investigate model performance across different findings, participating sites, countries, patient age groups and genders using either vendor-specified or Youdens-Index-adjusted thresholds. Table represents area under the curve with 95% confidence intervals in parentheses. The specific information pertaining to training and testing of the algorithm has been described in prior studies [21]. ; writingoriginal draft preparation, P.K. A study coinvestigator (PK: a second-year post-doctoral fellow in radiology) reviewed all 2600 CXR reports to exclude 163 CXR reports with description of a radiological finding in any section of the radiology reports (main body, findings or impression sections). 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. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner. 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). 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. Accuracy and area under the curve (AUC) of the AI algorithm based on Youdens-Index-based thresholds for different findings on CXRs. Flow diagram illustrating the patient selection, inclusion and exclusion criteria. 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. ; methodology, M.K.K. The lung nodules deemed as not important likely represented calcified granulomata. 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. Likewise, there were no significant differences in the performance of the AI algorithm between three different age groups (<40 years, 4165 years, >65 years) (p > 0.05) (Table 6). 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. Our study shows that the assessed AI algorithm could help to detect a substantial proportion of clinically important missed findings on CXRs. The most frequent and clinically important missed findings included lung nodules and consolidation at all eight participating sites in both India and the US. Data were analyzed to obtain area under the ROC curve (AUC). You may notice problems with 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). Indeed, 19% of early lung cancers that present as nodules on CXRs are missed [10]. This research received no external funding. Screen captures of the AI validation interface illustrating the scatterplots of AI output for gender-wise distribution of CXR findings (true positive (red dots), true negative (blue dots), false negative (yellow dots) and false positive (green dots)). Ueda D., Yamamoto A., Shimazaki A., Walston S.L., Matsumoto T., Izumi N., Tsukioka T., Komatsu H., Inoue H., Kabata D., et al. There were no significant differences in the AUCs for most findings with and without clinical importance (p > 0.16). Although the assessed AI model could evaluate more than 10 findings included in our study, we did not include other findings due to logistical challenges associated with the interpretation of unfunded studies. Tam et al. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. reported that their AI algorithm only detected 19.4% of the unreported lung nodules greater than 6 mm [26]. Not all missed findings are clinically important, but some missed CXR findings have serious implications. AI-detected CXR findings that were not documented in the radiology reports included pulmonary nodule (A), consolidation (B), pleural effusion (C), pneumothorax (D) and hilar prominence (E). 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. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Due to the non-interventional, retrospective nature of the study, need for written informed consent was waived. Licensee MDPI, Basel, Switzerland. https://creativecommons.org/licenses/by/4.0/. Since we assessed the use of only one AI model in our study, we cannot comment on the impact of applying more than one AI model on the overall reduction in missed finding frequency. Another study by Ahn et al. Common errors and pitfalls in interpretation of the adult chest radiograph. DICOM CXRs of 2407 patients were de-identified and exported offline. 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]. Next, we excluded all CXRs with identical medical records or examination numbers to avoid sharing any personal health identifying information across the sites. However, due to concerns over data privacy and security, multi-site, international studies with thousands of imaging studies are difficult and expensive. Wu J.T., Wong K.C.L., Gur Y., Ansari N., Karargyris A., Sharma A., Morris M., Saboury B., Ahmad H., Boyko O., et al. Ekpo E.U., Egbe N.O., Akpan B.E. The validation platform enabled seamless comparison of AI performance with both summary statistics (e.g., AUCs, accuracies) as well as individual case-level false positives, false negatives, true positives and true negatives. are employees of CARPL. The numbers within the parentheses represent 95% confidence intervals. Although the AUCs for standalone AI performance reported in our study are lower than those in prior studies [24], the assessed AI algorithm detected several missed findings not documented in the original radiology reports. 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). Figure 3 presents examples of missed findings on CXRs. Utility of artificial intelligence tool as a prospective radiology peer reviewerDetection of unreported intracranial hemorrhage. ; software, A.J., S.G., P.P., V.M., B.R., V.K.V. 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. AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. 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. Understanding and confronting our mistakes: The epidemiology of error in radiology and strategies for error reduction. Jones C.M., Danaher L., Milne M.R., Tang C., Seah J., Oakden-Rayner L., Johnson A., Buchlak Q.D., Esmaili N. Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: A real-world observational study. reported that AI detected 13.3% of false-negative CXRs in a dataset of 4208 CXRs [].Another study by Ahn et al. and M.K.K. 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). The need for written informed consent was waived. and K.J.D. Parisa Kaviani, Mannudeep K. Kalra, [], and Keith J. Dreyer. Table 1 and Table 2 summarize the distribution of findings without clinical importance (scores 1 and 2) and those with some clinical importance (scores 35). Deep learning in chest radiography: Detection of findings and presence of change. 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. (Key: NAnot. All authors have read and agreed to the published version of the manuscript. The functionality is limited to basic scrolling. Brunese L., Mercaldo F., Reginelli A., Santone A. Data from the year 2010 reported 183 million radiographic examinations in the United States alone -, with CXRs representing up to 44% of all radiographs [4]. 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). Our research has some limitations. and V.V.) Another implication of our study is the high rate of missed CXR findings at all sites, which is neither a new nor a groundbreaking discovery but stresses the role of AI algorithms in reducing the frequency of such missed findingsat least those deemed clinically important. 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%). Conceptualization, P.K. 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%). Radiographers performance in chest X-ray interpretation: The Nigerian experience. The AI outputs were imported into the CARPL platform for data analysis and visualization. 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]. Publishers Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Another CXR study reported that standalone AI performance for pneumothorax, pleural effusion and lung lesions was similar to that for radiology residents, but was significantly better than the performance of non-radiology residents [19]. Killock D. AI outperforms radiologists in mammographic screening. All 2407 deidentified frontal CXRs were processed with the AI algorithm (Qure.ai). Finally, given the inter-observer variations in radiologists interpretation of CXRs, ground-truthing was performed by only two radiologists. Artificial intelligence system for identification of false-negative interpretations in chest radiographs. Accuracy and area under the curve (AUC) of the AI algorithm based on vendor-based thresholds for different findings on CXRs. also reported the improved detection of suspicious pulmonary nodules on CXR with AI-aided interpretation (sensitivities 8994%) versus unaided reporting interpretation for all three radiologists (sensitivities 6986%), with a slight increase in false positives and a decrease in specificity [18]. already built in. Although there are multiple prior publications on AI performance, to our best knowledge there are sparse data on the performance of AI algorithms on missed radiological findings. and M.T. The Human Research Committee of our Institutional Review Board approved the study. and P.K. The discordance between radiologists and physicians in one prospective study was 12.5% for CXRs reported as normal by physicians but abnormal in the opinion of radiologists [6]. For each missed finding, the two radiologists also drew an annotation box within the CARPL Platform (Figure 2) around the finding and gave a score for the perceived clinical importance of the missed finding (1: not clinically important; 2: unlikely of clinical importance; 3: borderline clinical importance; 4: moderate clinical importance; 5: critically important finding). 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. (Key: NAnot applicable because there was no missed pneumothorax in patients over 65 years. Rao B., Zohrabian V., Cedeno P., Saha A., Pahade J., Davis M.A. and M.K.K. Table 3 summarizes country-wise distribution of CXR findings at the vendor-recommended thresholds. Beyond CXRs, other studies have reported on missed findings of intracranial hemorrhage in noncontract head CT examinations and mammography [20]. ; resources, P.K. The study data comprised 2407 CXRs from 2407 adult patients (mean age [ standard deviation] 39 [17] years; malefemale ratio 1248:1159) who had a CXR between 2015 and 2021 at one of eight healthcare sites in India (3 sites) or the United States (5 sites) (Figure 1). You may switch to Article in classic view. The incremental value of AI for interpreting CXRs in our study follows the trends reported in other AI studies [23,25]. With the ground truth, there were 410 CXRs (17.1%, 410/2407), with missed findings in 342/2407 CXRs (14.2% missed finding rate). 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