Image sets, both complete and partial, formed the basis for the models that perform detection, segmentation, and classification. Analyses of precision, recall, the Dice coefficient, and the area under the ROC curve (AUC) were used to evaluate model performance. Three radiologists (three senior and three junior) were involved in a comparison of three AI-assisted diagnostic strategies (without AI, with freestyle AI assistance, and with rule-based AI assistance) to achieve optimal integration into clinical practice. Results: A total of 10,023 patients, with a median age of 46 years (interquartile range 37-55 years), and 7,669 females, were included in the study. In the detection, segmentation, and classification models, the average precision, Dice coefficient, and AUC results were 0.98 (95% confidence interval 0.96 to 0.99), 0.86 (95% CI 0.86 to 0.87), and 0.90 (95% CI 0.88 to 0.92), respectively. immediate allergy The segmentation model, trained on nationwide data, and the classification model, trained on data from multiple vendors, presented the best performance indicators, characterized by a Dice coefficient of 0.91 (95% CI 0.90, 0.91) and an AUC of 0.98 (95% CI 0.97, 1.00), respectively. The AI model surpassed all senior and junior radiologists in performance (P less than .05 for all comparisons), demonstrating improved diagnostic accuracy for all radiologists aided by rule-based AI assistance (P less than .05 for all comparisons). Thyroid ultrasound AI models, developed using data from various sources, demonstrated impressive diagnostic precision among individuals of Chinese descent. Radiologists' effectiveness in diagnosing thyroid cancer cases was boosted by rule-based AI assistance tools. Supplementary material for this article, from the RSNA 2023 conference, is now accessible.

A significant portion, roughly half, of adults with chronic obstructive pulmonary disease (COPD) lack a formal diagnosis. COPD detection is possible through the frequent acquisition of chest CT scans in clinical practice. This study will evaluate the diagnostic capability of radiomics features in COPD using CT scans of both standard and reduced radiation doses. In this secondary analysis, participants from the Genetic Epidemiology of COPD (COPDGene) study, who underwent an initial assessment at baseline (visit 1) and a follow-up assessment ten years later (visit 3), were included. The characteristic spirometric finding of COPD was a forced expiratory volume in one second relative to forced vital capacity falling below 0.70. We examined the performance of demographic characteristics, CT emphysema percentages, radiomic features, and a composite feature set developed from the analysis of only inspiratory CT scans. To detect COPD, two classification experiments utilizing CatBoost (a gradient boosting algorithm from Yandex) were conducted. Model I was trained and tested using standard-dose CT data from visit 1, while Model II used low-dose CT data from visit 3. genetic exchange An assessment of model classification performance was conducted using the area under the receiver operating characteristic curve (AUC) and precision-recall curve analysis metrics. The evaluation involved 8878 participants, with a mean age of 57 years and 9 standard deviations, comprised of 4180 females and 4698 males. Model I's radiomics features demonstrated an AUC of 0.90 (95% CI 0.88 to 0.91) in the standard-dose CT cohort, surpassing the performance of demographics (AUC 0.73; 95% CI 0.71 to 0.76; p < 0.001). A significant correlation was observed between emphysema and the AUC value (AUC, 0.82; 95% confidence interval 0.80 to 0.84; p < 0.001). The combined features, evidenced by AUC (0.90), with a 95% confidence interval of 0.89 to 0.92 and a p-value of 0.16, were observed. On a 20% held-out test set, Model II, trained on low-dose CT scans using radiomics features, achieved an AUC of 0.87 (95% CI 0.83, 0.91) compared to 0.70 (95% CI 0.64, 0.75) for demographics, with a statistically significant difference (p = 0.001). Emphysema percentage (AUC=0.74; 95% CI=0.69-0.79; P=0.002) was a significant finding. A statistical analysis of the combined features showed an AUC of 0.88 (confidence interval: 0.85–0.92), yielding a non-significant p-value of 0.32. In the standard-dose model, density and texture features prominently comprised the top 10 characteristics, contrasting with the low-dose CT model, where lung and airway shapes were key contributors. Inspiratory CT scans reveal a combination of lung and airway features, including parenchymal texture and shape, allowing for accurate COPD detection. Transparency in clinical trials is enhanced through the online resource offered by ClinicalTrials.gov. The registration number should be returned. The NCT00608764 RSNA 2023 article's accompanying supplemental data is now publicly accessible. WH-4-023 datasheet Refer also to Vliegenthart's editorial in this publication.

A novel photon-counting CT technology might offer enhanced noninvasive evaluation of patients highly susceptible to coronary artery disease (CAD). This research sought to establish the diagnostic power of ultra-high-resolution coronary computed tomography angiography (CCTA) for the detection of coronary artery disease (CAD), as compared to the gold standard of invasive coronary angiography (ICA). Participants with severe aortic valve stenosis, whose clinical needs necessitated CT scans for transcatheter aortic valve replacement planning, were enrolled consecutively in this prospective study from August 2022 to February 2023. All participants underwent dual-source photon-counting CT scans guided by a retrospective electrocardiography-gated contrast-enhanced UHR scanning protocol (120 or 140 kV; 120 mm; 100 mL iopromid; omitting spectral data). Subjects' clinical routines were augmented by ICA procedures. A consensus determination of image quality, using a five-point Likert scale (1 = excellent [absence of artifacts], 5 = nondiagnostic [severe artifacts]), and a separate, masked reading for the presence of coronary artery disease (50% stenosis), were simultaneously executed. Utilizing the area under the ROC curve (AUC), UHR CCTA was assessed against ICA. Within the group of 68 participants (mean age 81 years, 7 [SD]; 32 male, 36 female), the prevalence of coronary artery disease (CAD) was 35% and prior stent placement, 22%. The image quality was remarkably consistent, with a median score of 15 and an interquartile range from 13 to 20, representing excellent results overall. In assessing coronary artery disease (CAD), UHR CCTA yielded an area under the curve (AUC) of 0.93 per participant (95% confidence interval: 0.86-0.99), 0.94 per vessel (95% confidence interval: 0.91-0.98), and 0.92 per segment (95% confidence interval: 0.87-0.97). Across participants (n = 68), the values for sensitivity, specificity, and accuracy were 96%, 84%, and 88%, respectively. For vessels (n = 204), the corresponding values were 89%, 91%, and 91%, and for segments (n = 965), the values were 77%, 95%, and 95%. UHR photon-counting CCTA exhibited high diagnostic accuracy in identifying CAD among a high-risk population, featuring subjects with severe coronary calcification or a previous stent procedure, proving a useful diagnostic tool. A Creative Commons Attribution 4.0 International license governs this publication. Supporting documentation for this article is available. The Williams and Newby editorial is featured in this issue, be sure to view it.

Handcrafted radiomics and deep learning models, individually, demonstrate strong performance in differentiating benign and malignant lesions on contrast-enhanced mammograms. The focus of this research is to build a comprehensive machine learning tool that automatically identifies, segments, and categorizes breast lesions observed in CEM images of patients who have been recalled. From 2013 to 2018, a retrospective review of CEM images and clinical details was undertaken for 1601 patients at Maastricht UMC+ and 283 patients at the Gustave Roussy Institute for external verification. Lesions with a pre-determined status, either malignant or benign, were accurately delineated by a research assistant, who was mentored by an expert breast radiologist. A deep learning model designed to automatically identify, segment, and classify lesions was trained on preprocessed low-energy images, along with recombined ones. A handcrafted radiomics model was also trained to categorize lesions that were segmented using both human and deep learning methodologies. The sensitivity for identification and the area under the receiver operating characteristic curve (AUC) for classification were contrasted between individual and combined models, specifically for image and patient-specific data sets. Following the removal of patients lacking suspicious lesions, the training, testing, and validation datasets comprised 850 patients (mean age 63 ± 8 years), 212 patients (mean age 62 ± 8 years), and 279 patients (mean age 55 ± 12 years), respectively. The external dataset's lesion identification sensitivity was 90% at the image level and 99% at the patient level. The mean Dice coefficient was 0.71 at the image level and 0.80 at the patient level. Manual segmentations facilitated the highest AUC (0.88 [95% CI 0.86, 0.91]) for the combined deep learning and handcrafted radiomics classification model, a result significant at P < 0.05. The P-value of .90 highlights a difference in comparison to deep learning (DL), manually crafted radiomics, and clinical characteristics models. Segmentations generated via deep learning, when integrated with a handcrafted radiomics model, exhibited the highest AUC (0.95 [95% CI 0.94, 0.96]), reaching statistical significance (P < 0.05). Suspicious lesions in CEM images were accurately recognized and outlined by the deep learning model, with the combined output of the deep learning and handcrafted radiomics models showcasing impressive diagnostic performance. Access to the supplementary materials for the RSNA 2023 article is now possible. For further insight, refer to Bahl and Do's editorial in this issue.