Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction.

Background Multiple commercial artificial intelligence (AI) products exist for assessing radiographs; however, comparable performance data for these algorithms are limited. Purpose To perform an independent, stand-alone validation of commercially available AI products for bone age prediction based on hand radiographs and lung nodule detection on chest radiographs. Materials and Methods This retrospective study was carried out as part of Project AIR. Nine of 17 eligible AI products were validated on data from seven Dutch hospitals. For bone age prediction, the root mean square error (RMSE) and Pearson correlation coefficient were computed. The reference standard was set by three to five expert readers. For lung nodule detection, the area under the receiver operating characteristic curve (AUC) was computed. The reference standard was set by a chest radiologist based on CT. Randomized subsets of hand (n = 95) and chest (n = 140) radiographs were read by 14 and 17 human readers, respectively, with varying experience. Results Two bone age prediction algorithms were tested on hand radiographs (from January 2017 to January 2022) in 326 patients (mean age, 10 years ± 4 [SD]; 173 female patients) and correlated strongly with the reference standard (r = 0.99; P < .001 for both). No difference in RMSE was observed between algorithms (0.63 years [95% CI: 0.58, 0.69] and 0.57 years [95% CI: 0.52, 0.61]) and readers (0.68 years [95% CI: 0.64, 0.73]). Seven lung nodule detection algorithms were validated on chest radiographs (from January 2012 to May 2022) in 386 patients (mean age, 64 years ± 11; 223 male patients). Compared with readers (mean AUC, 0.81 [95% CI: 0.77, 0.85]), four algorithms performed better (AUC range, 0.86-0.93; P value range, <.001 to .04). Conclusions Compared with human readers, four AI algorithms for detecting lung nodules on chest radiographs showed improved performance, whereas the remaining algorithms tested showed no evidence of a difference in performance. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Omoumi and Richiardi in this issue.

Imaging of nontraumatic thoracic emergencies.

PURPOSE OF REVIEW: Acute chest symptoms form an important incentive for imaging in the emergency setting. This review discusses the radiologic features of various vascular and pulmonary diseases leading to acute respiratory distress and recent developments on important emergency radiologic examinations. RECENT FINDINGS: Recently, triple-rule-out computed tomography protocol was introduced in diagnosis of chest pain, and advancing computed tomography technology and knowledge have led to discussion on treatment of pulmonary embolism. Diffuse pulmonary opacities remain a diagnostic dilemma in the emergency setting and although imaging findings can often be nonspecific, they help in guiding toward accurate diagnosis and timely management. SUMMARY: Though promising, triple-rule-out is not yet justified because of low incidence of additional findings compared with conventional computed tomography angiography in chest pain, but it might be suited for clinical practice in the near future. Relevance of isolated subsegmental pulmonary embolism is unknown and research on this topic is needed and on its way. We provided some key findings in differentiating diffuse pulmonary opacities and describe the additional value of chest ultrasound in this clinical dilemma. A brief sidestep to pneumothorax is made, as this is also a frequent finding in the acute dyspneic patient, as well as in patients with acute chest pain.

The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study.

PURPOSE: The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the detection of common chest abnormalities. MATERIALS AND METHODS: A total of 261 posteroanterior and lateral chest radiographs were collected from 2 hospitals. Radiographs could contain single or multiple focal opacities <3 cm (n=66), single or multiple focal opacities >3 cm (n=33), diffuse lung disease (n=49), signs of cardiogenic congestion (n=26), or no abnormalities (n=110). Twenty-one cases contained >1 type of disease. All abnormalities were confirmed by a computed tomographic scan obtained within 4 weeks of the radiograph. Bone-suppressed images (BSIs) were generated from every posteroanterior radiograph (ClearRead BSI 3.2). All cases were read by 6 radiologists without BSI, followed by an evaluation of the same case with BSI. Presence or absence of each disease category and confidence (0-100) of the observers were documented for each interpretation. Differences in the number of correct detections without and with BSI were analyzed using the Wilcoxon signed-rank test. RESULTS: On average, 6 more cases with focal lesions were correctly identified with BSI (P=0.03), and 1 additional case with diffuse abnormalities was found with BSI (P=0.32). None of the observers demonstrated a decrease in the number of correctly detected cases with diffuse abnormalities or cardiogenic congestion with BSI. False positives in normal cases with availability of BSI mainly referred to the detection of small focal lesions (on average 7 per reader; P=0.04). CONCLUSIONS: BSI does not negatively affect the interpretation of diffuse lung disease, while improving visualization of focal lesions on chest radiographs. BSI leads to overcalling of focal abnormalities in normal radiographs.

The reliability of magnetic resonance imaging in traumatic brain injury lesion detection.

OBJECTIVE: This study compares inter-rater-reliability, lesion detection and clinical relevance of T2-weighted imaging (T2WI), Fluid Attenuated Inversion Recovery (FLAIR), T2*-gradient recalled echo (T2*-GRE) and Susceptibility Weighted Imaging (SWI) in Traumatic Brain Injury (TBI). METHODS: Three raters retrospectively scored 56 TBI patients' MR images (12-76 years old, median TBI-MRI interval 7 weeks) on number, volume, location and intensity. Punctate lesions (diameter <10 mm) were scored separately from large lesions (diameter ≥ 10 mm). Injury severity was assessed with the Glasgow Coma Scale (GCS), outcome with the Glasgow Outcome Scale-Extended (GOSE). RESULTS: Inter-rater-reliability for lesion volume and punctate lesion count was good (ICC = 0.69-0.94) except for punctate lesion count on T2WI (ICC = 0.19) and FLAIR (ICC = 0.15). SWI showed the highest number of lesions (mean = 30.0), followed by T2*-GRE (mean = 15.4), FLAIR (mean = 3.1) and T2WI (mean = 2.2). Sequences did not differ in detected lesion volume. Punctate lesion count on T2*-GRE (r = -0.53) and SWI (r = -0.49) correlated with the GCS (p < 0.001). CONCLUSIONS: T2*-GRE and SWI are more sensitive than T2WI and FLAIR in detecting (haemorrhagic) traumatic punctate lesions. The correlation between number of punctate lesions on T2*-GRE/SWI and the GCS indicates that haemorrhagic lesions are clinically relevant. The considerable inter-rater-disagreement in this study advocates cautiousness in interpretation of punctate lesions using T2WI and FLAIR.