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Likewise, in a real-world dataset of 2972 CXRs, Jones et al. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: A retrospective clinical validation study. Generating an ePub file may take a long time, please be patient. Berlin L. Reporting the missed radiologic diagnosis: Medicolegal and ethical considerations. ; supervision, M.K.K. Radiologic errors in patients with lung cancer. These included pulmonary nodule (A), consolidation (B), pleural effusion (C), pneumothorax (D) and hilar prominence (E). Following post-processing of the test datasets, the AI algorithm generated an Excel file with information on model outputs for specific CXR findings based on the probability scores from zero to one hundred. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. ; writingoriginal draft preparation, P.K. The ePub format is best viewed in the iBooks reader. Since these findings were not reported during clinical interpretation, they were labeled as missed findings. already built in. Artificial intelligence system for identification of false-negative interpretations in chest radiographs. and M.J.) are employees of Qure.ai. A novel abnormality annotation database for COVID-19 affected frontal lung x-rays. AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. Brunese L., Mercaldo F., Reginelli A., Santone A. We limited the evaluation to these findings because they represented the key detectable findings for the assessed AI algorithm (Qure.ai, Mumbai, India) on CXRs. Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Several missed findings such as pneumothoraces, pleural effusions and rib fractures were rare (n < 11) in our study sample, and therefore it is difficult to assess the performance of the AI model for such findings. Disagreements between the two radiologists were resolved in a consensus, joint review to establish the final ground truth. 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. There were no significant differences in AI performance based on country or gender (Table 5) (p > 0.1). 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. SG, VM and VV are employees of Caring Inc. Other coauthors have no pertinent disclosures. A high frequency of missed lung nodules on CXRs has also been reported in prior studies [23]. Another limitation of our study is the lack of pediatric CXRs, since the assessed AI model was not trained with adequate pediatric CXRs. 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). Common errors and pitfalls in interpretation of the adult chest radiograph. and P.K. Two coauthors (MKK: Coreline Soft Inc., Seoul, South Korea; Riverain Tech., Miamisburg, OH, USA; Siemens Healthineers, Erlangen, Germany; SRD: Lunit Inc., Seoul, Korea; Qure.ai, Mumbai, India) received industrial research grants for unrelated research. This research received no external funding. However, both radiologists had multiple years of experience as practicing thoracic radiologists and fellowship training in thoracic imaging. 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. 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]. Beyond CXRs, other studies have reported on missed findings of intracranial hemorrhage in noncontract head CT examinations and mammography [20]. 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). Accuracy and area under the curve (AUC) of the AI algorithm based on Youdens-Index-based thresholds for different findings on CXRs. Users of AI models should be aware of the impact of such variations on their local CXRs. Data were analyzed to obtain area under the ROC curve (AUC). The numbers within the parentheses represent 95% confidence intervals. ; funding acquisition, A.J. Thus, our final study sample size was 2407 CXRs (1262 CXRs from India; 1145 CXRs from US) (Figure 1). 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). Related Work. 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). A study co-investigator (S.R.D.) Despite its overwhelming use, CXR interpretation is subjective and prone to wide interobserver inconsistencies based on readers knowledge and experience [5,6,7]. 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. DICOM CXRs of 2407 patients were de-identified and exported offline. 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). 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. There were no significant differences in the AUCs for most findings with and without clinical importance (p > 0.16). Table represents area under the curve with 95% confidence intervals in parentheses. 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. 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%). The resulting data were de-identified and populated into a single Microsoft Excel file (Microsoft Inc. (Redmond, WA, USA)). 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]. ; resources, P.K. 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]. 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? The specific information pertaining to training and testing of the algorithm has been described in prior studies [21]. Diagnostics (Basel). 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]. Our study demonstrates that a substantial number of clinically important findings are missed on CXRs, regardless of practice type and location. Validation of AI models across diverse datasets is critical for establishing their generalizability. 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). The ePub format uses eBook readers, which have several "ease of reading" features Consequently, the geo-racial variations reported in our study across the US and India could have led to an under- or overestimate of AI performance. There are also substantial variations among radiologists, with a misinterpretation rate for CXRs as high as 30% in a prior study [8,9]. The AI algorithms can identify patterns and perform complex computational operations more rapidly and precisely than humans [11]. Not all missed findings are clinically important, but some missed CXR findings have serious implications. Next, we excluded all CXRs with identical medical records or examination numbers to avoid sharing any personal health identifying information across the sites. 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. 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. The data from each site with the radiology reports were exported in tabular form. We report on methods and platforms for assessing variations in AI performance based on geographic location, type of hospital setting, patient gender and age group for different types of CXR findings. 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. In addition, the platform provided an interactive scatter plot to identify the distribution of false-positive and false-negative findings. 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. According to some estimates, CXRs represent up to 20% of all imaging exams [3]. and M.K.K. The ground-truth radiologists had no access to AI output at the time of interpretation. The most frequent and clinically important missed findings included lung nodules and consolidation at all eight participating sites in both India and the US. The numbers within the parentheses represent 95% confidence intervals. ; visualization, M.K.K. 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. 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)). Rudolph J., Schachtner B., Fink N., Koliogiannis V., Schwarze V., Goller S., Trappmann L., Hoppe B.F., Mansour N., Fischer M., et al. Figure 4 presents findings missed by both the AI algorithm and in the original radiology reports. We obtained approval from the Human Research Committee of our Institutional Review Board (Mass General Brigham) (protocol code 2020P003950, approval date 23 December 2020). 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. ; methodology, M.K.K. We hypothesized that an AI algorithm can reduce missed findings on CXRs. Tam et al. Another study by Ahn et al. has an unrelated research grant from Siemens Healthineers, Riverain Tech and Coreline Inc. Four of the co-authors (A.J., P.P., B.R. Among the US sites, there were two quaternary hospitals (Massachusetts General Hospital and Brigham Womens Hospital; both in Boston MA) and three community hospitals (Cooley Dickinson Hospital, Northampton, MA, USA; Newton-Wellesley Hospital, Newton, MA, USA; Salem Hospital, Salem, MA, USA). Our study shows that the assessed AI algorithm could help to detect a substantial proportion of clinically important missed findings on CXRs. Table represents area under the curve with 95% confidence intervals in parentheses. 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. The need for written informed consent was waived. Halvorsen J.G., Kunian A. 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