T he accurate determination of a child's developmental status is required for proper treatment of various growth disorders (1) and scoliosis (2). Other parameters, such as height, weight, secondary sexual characteristics, chronologic age, and dental age, correlate with developmental status, but skeletal age has been considered the most reliable method (3-5). The standard of care for this assessment calls for radiologists to identify the reference standard in an atlas of hand radiographs that most closely resembles an anteroposterior or posteroanterior radiograph of the participant's left hand. The most common atlas used as a reference standard is the Radiographic Atlas of Skeletal Development of the Hand and Wrist, published in 1959 (6).As part of the process of implementing an artificial intelligence (AI) algorithm in clinical practice, it is critical to properly determine its effects. However, different study designs may yield different findings about the same assistive technologies. For example, the same commercially available computer-aided detection system for detecting pulmonary nodules on chest CT scans produced different findings in studies completed within a year of each other (7-9). Findings on potential computer-aided diagnosis Background: Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.Purpose: To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods:In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center-and radiologist-level effects was used to compare the two experimental groups.Results: Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion:Use of an artificial intelligence algorithm ...

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen’s Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80–100% and 87–100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71–94% and positive predictive values in the range of 88–100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.

Candida albicans invades endothelial cells by binding to N-cadherin and other cell surface receptors. This binding induces rearrangement of endothelial cell actin microfilaments, which results in the formation of pseudopods that surround the organism and pull it into the endothelial cell. Here, we investigated the role of endothelial cell septin 7 (SEPT7) in the endocytosis of C. albicans hyphae. Using confocal microscopy, we determined that SEPT7 accumulated with N-cadherin and actin microfilaments around C. albicans as it was endocytosed by endothelial cells. Affinity purification studies indicated that a complex containing N-cadherin and SEPT7 was recruited by C. albicans and that formation of this complex around C. albicans was mediated by the fungal Als3 and Ssa1 invasins. Knockdown of N-cadherin by small interfering RNA (siRNA) reduced recruitment of SEPT7 to C. albicans, suggesting that N-cadherin functions as a link between SEPT7 and the fungus. Also, depolymerization of actin microfilaments with cytochalasin D decreased the association between SEPT7 and N-cadherin and inhibited recruitment of both SEPT7 and N-cadherin to C. albicans, indicating the necessity of an intact cytoskeleton in the functional interaction between SEPT7 and N-cadherin. Importantly, knockdown of SEPT7 decreased accumulation of N-cadherin around C. albicans in intact endothelial cells and reduced binding of N-cadherin to this organism, as revealed by the affinity purification assay. Furthermore, SEPT7 knockdown significantly inhibited the endocytosis of C. albicans. Therefore, in response to C. albicans infection, SEPT7 forms a complex with endothelial cell N-cadherin, is required for normal accumulation of N-cadherin around C. albicans hyphae, and is necessary for maximal endocytosis of the organism.

Background: Coronary artery calcium (CAC) can be identified on non-gated chest CTs, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of non-gated chest CTs. Our objective was to evaluate the impact of notifying clinicians and patients of incidental CAC on statin initiation. Methods: NOTIFY-1 was a randomized quality improvement project in the Stanford healthcare system. Patients without known atherosclerotic cardiovascular disease (ASCVD) or prior statin prescription were screened for CAC on a prior non-gated chest CT from 2014-2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomized to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. Results: Among 2,113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After additional exclusions following chart review, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomized to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (p<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] vs. 2.3% [2/87], p=0.008). Conclusions: Opportunistic CAC screening of prior non-gated chest CTs followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce ASCVD events.