Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of rebleeding persists long thereafter. Increases in mean lesional quantitative susceptibility mapping (QSM) ≥ 6% on MRI during 1 year of prospective surveillance have been associated with new symptomatic hemorrhage (SH) during that period. The authors hypothesized that QSM at a single time point reflects features of hemorrhage in the prior year or potential bleeding in the subsequent year. Twenty-eight features were extracted from 265 QSM acquisitions in 120 patients enrolled in a prospective trial readiness project, and machine learning methods examined associations with SH and biomarker bleed (QSM increase ≥ 6%) in prior and subsequent years. QSM features including sum variance, variance, and correlation had lower average values in lesions with SH in the prior year (p < 0.05, false discovery rate corrected). A support-vector machine classifier recurrently selected sum average, mean lesional QSM, sphericity, and margin sharpness features to distinguish biomarker bleeds in the prior year (area under the curve = 0.61, 95% CI 0.52-0.70; p = 0.02). No QSM features were associated with a subsequent bleed. These results provide proof of concept that machine learning may derive features of QSM reflecting prior hemorrhagic activity, meriting further investigation. Clinical trial registration no.: NCT03652181 (ClinicalTrials.gov).

Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strategy for managing pain and preventing neuroma formation after amputation. This shift in TMR's motivation has influenced surgical approaches, in ways that may challenge conventional electromyography (EMG)-based prosthetic control. The primary goal is often to simply reinnervate nerves to accessible muscles. This contrasts the earlier, more complex TMR surgeries that optimize EMG signal detection by carefully selecting target muscles near the skin's surface and manipulate residual anatomy to electrically isolate muscle activity. Consequently, modern TMR surgeries can involve less consideration for factors such as the depth of the reinnervated muscles or electrical crosstalk between closely located reinnervated muscles, all of which can impair the effectiveness of conventional prosthetic control systems. We recruited 4 participants with TMR, varying levels of upper limb loss, and diverse sets of reinnervated muscles. Participants attempted performing movements with their missing hands and we used a muscle activity measurement technique that employs ultrasound imaging and machine learning (sonomyography) to classify the resulting muscle movements. We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. Our findings suggest that despite the shifting motivations for performing TMR surgery this new generation of the surgical procedure not only offers prophylactic benefits but also retains promising opportunities for bionic prosthetic control.

Deep learning (DL) reconstruction shows potential in reducing MRI acquisition times while preserving image quality, but the impact of varying acceleration factors on knee MRI diagnostic accuracy remains undefined. Evaluate diagnostic performance of twofold, fourfold, and sixfold DL-accelerated knee MRI protocols versus standard protocols. In this prospective study, 71 consecutive patients underwent knee MRI with standard, DL2, DL4, and DL6 accelerated protocols. Four radiologists assessed ligament tears, meniscal lesions, bone marrow edema, chondropathy, and extensor abnormalities. Sensitivity, specificity, and interobserver agreement were calculated. DL2 and DL4 demonstrated high diagnostic accuracy. For ACL tears, DL2/DL4 achieved 98-100% sensitivity/specificity, while DL6 showed reduced sensitivity (91-96%). In meniscal evaluation, DL2 maintained 96-100% sensitivity and 98-100% specificity; DL4 showed 94-98% sensitivity and 97-99% specificity. DL6 exhibited decreased sensitivity (82-92%) for subtle lesions. Bone marrow edema detection remained excellent across acceleration factors. Interobserver agreement was excellent for DL2/DL4 (W = 0.91-0.97) and good for DL6 (W = 0.78-0.89). DL2 protocols demonstrate performance nearly identical to standard protocols, while DL4 maintains acceptable diagnostic accuracy for most pathologies. DL6 shows reduced sensitivity for subtle abnormalities, particularly among less experienced readers. DL2 and DL4 protocols represent optimal balance between acquisition time reduction (50-75%) and diagnostic confidence.

Urinary tract dilation (UTD) is a frequent problem in infants. Automated and objective classification of UTD from renal ultrasounds would streamline their interpretations. To develop and evaluate the performance of different deep learning models in predicting UTD classifications from renal ultrasound images. We searched our image archive to identify renal ultrasounds performed in infants ≤ 3-months-old for the clinical indications of prenatal UTD and urinary tract infection (9/2023-8/2024). An expert pediatric uroradiologist provided the ground truth UTD labels for representative sagittal sonographic renal images. Three different deep learning models trained with cross-entropy loss were adapted with four-fold cross-validation experiments to determine the overall performance. Our curated database included 492 right and 487 left renal ultrasounds (mean age ± standard deviation = 1.2 ± 0.1 months for both cohorts, with 341 boys/151 girls and 339 boys/148 girls, respectively). The model prediction accuracies for the right and left kidneys were 88.7% (95% confidence interval [CI], [85.8%, 91.5%]) and 80.5% (95% CI, [77.6%, 82.9%]), with weighted kappa scores of 0.90 (95% CI, [0.88, 0.91]) and 0.87 (95% CI, [0.82, 0.92]), respectively. When predictions were binarized into mild (normal/P1) and severe (UTD P2/P3) dilation, accuracies of the right and left kidneys increased to 96.3% (95% CI, [94.9%, 97.8%]) and 91.3% (95% CI, [88.5%, 94.2%]), but agreements decreased to 0.78 (95% CI, [0.73, 0.82]) and 0.75 (95% CI, [0.68, 0.82]), respectively. Deep learning models demonstrated high accuracy and agreement in classifying UTD from infant renal ultrasounds, supporting their potential as decision-support tools in clinical workflows.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations. Efforts to quantify its various components using CT imaging have advanced, aiming for more precise, objective, and reproducible assessment and management. Beyond emphysema and small airway disease, the two major components of COPD, CT quantification enables the evaluation of pulmonary vascular alteration, ventilation-perfusion mismatches, fissure completeness, and extrapulmonary features such as altered body composition, osteoporosis, and atherosclerosis. Recent advancements, including the application of deep learning techniques, have facilitated fully automated segmentation and quantification of CT parameters, while innovations such as image standardization hold promise for enhancing clinical applicability. Numerous studies have reported associations between quantitative CT parameters and clinical or physiologic outcomes in patients with COPD. However, barriers remain to the routine implementation of these technologies in clinical practice. This review highlights recent research on COPD quantification, explores advances in technology, and also discusses current challenges and potential solutions for improving quantification methods.

Multiparametric MRI (mpMRI) has become an essential tool in the detection of prostate cancer (PCa) and can help many men avoid unnecessary biopsies. However, interpreting prostate mpMRI remains subjective, labor-intensive, and more complex compared to traditional transrectal ultrasound. These challenges will likely grow as MRI is increasingly adopted for PCa screening and diagnosis. This development has sparked interest in non-invasive artificial intelligence (AI) support, as larger and better-labeled datasets now enable deep-learning (DL) models to address important tasks in the prostate MRI workflow. Specifically, DL classification networks can be trained to differentiate between benign tissue and PCa, identify non-clinically significant disease versus clinically significant disease, and predict high-grade cancer at both the lesion and patient levels. This review focuses on the integration of DL classification networks with mpMRI for PCa assessment, examining key network architectures and strategies, the impact of different MRI sequence inputs on model performance, and the added value of incorporating domain knowledge and clinical information into MRI-based DL classifiers. It also highlights reported comparisons between DL models and the Prostate Imaging Reporting and Data System (PI-RADS) for PCa diagnosis and the potential of AI-assisted predictions, alongside ongoing efforts to improve model explainability and interpretability to support clinical trust and adoption. It further discusses the potential role of DL-based computer-aided diagnosis systems in improving the prostate MRI reporting workflow while addressing current limitations and future outlooks to facilitate better clinical integration of these systems. Evidence Level: N/A. Technical Efficacy: Stage 2.

Artificial intelligence (AI) holds significant promise for medical applications, particularly in coronary computed tomography angiography (CTA). We assessed the knowledge, attitudes, and practices (KAP) of cardiovascular health care personnel regarding coronary CTA and AI-assisted diagnosis. We conducted a cross-sectional survey from 1 July to 1 August 2024 at Tsinghua University Hospital, Beijing, China. Healthcare professionals, including both physicians and nurses, aged ≥18 years were eligible to participate. We used a structured questionnaire to collect demographic information and KAP scores. We analysed the data using correlation and regression methods, along with structural equation modelling. Among 496 participants, 58.5% were female, 52.6% held a bachelor's degree, and 40.7% worked in radiology. Mean KAP scores were 13.87 (standard deviation (SD) = 4.96, possible range = 0-20) for knowledge, 28.25 (SD = 4.35, possible range = 8-40) for attitude, and 31.67 (SD = 8.23, possible range = 10-50) for practice. Knowledge (r = 0.358; P < 0.001) and attitude positively correlated with practice (r = 0.489; P < 0.001). Multivariate logistic regression indicated that educational level, department affiliation, and job satisfaction were significant predictors of knowledge. Attitude was influenced by marital status, department, and years of experience, while practice was shaped by knowledge, attitude, departmental factors, and job satisfaction. Structural equation modelling showed that knowledge was directly affected by gender (β = -0.121; P = 0.009), workplace (β = -0.133; P = 0.004), department (β = -0.197; P < 0.001), employment status (β = -0.166; P < 0.001), and night shift frequency (β = 0.163; P < 0.001). Attitude was directly influenced by marriage (β = 0.124; P = 0.006) and job satisfaction (β = -0.528; P < 0.001). Practice was directly affected by knowledge (β = 0.389; P < 0.001), attitude (β = 0.533; P < 0.001), and gender (β = -0.092; P = 0.010). Additionally, gender (β = -0.051; P = 0.010) and marriage (β = 0.066; P = 0.007) had indirect effects on practice. Cardiovascular health care personnel exhibited suboptimal knowledge, positive attitudes, and relatively inactive practices regarding coronary CTA and AI-assisted diagnosis. Targeted educational efforts are needed to enhance knowledge and support the integration of AI into clinical workflows.

Coronary artery calcification (CAC) provides robust prediction for major adverse cardiovascular events (MACE), but current techniques disregard plaque distribution and protective effects of high CAC density. We investigated whether a novel CAC-dispersion and density (CAC-DAD) score will exhibit superior prognostic value compared with the Agatston score (AS) for MACE prediction. We conducted a multicenter, retrospective, cross-sectional study of 961 patients (median age, 67 years; 61% male) who underwent cardiac computed tomography for cardiovascular or perioperative risk assessment. Blinded analyzers applied deep learning algorithms to noncontrast scans to calculate the CAC-DAD score, which adjusts for the spatial distribution of CAC and assigns a protective weight factor for lesions with ≥1000 Hounsfield units. Associations were assessed using frailty regression. Over a median follow-up of 30 (30-460) days, 61 patients experienced MACE (nonfatal myocardial infarction or cardiovascular mortality). An elevated CAC-DAD score (≥2050 based on optimal cutoff) captured more MACE than AS ≥400 (74% versus 57%; <i>P</i>=0.002). Univariable analysis revealed that an elevated CAC-DAD score, AS ≥400 and AS ≥100, age, diabetes, hypertension, and statin use predicted MACE. On multivariable analysis, only the CAC-DAD score (hazard ratio, 2.57 [95% CI, 1.43-4.61]; <i>P</i>=0.002), age, statins, and diabetes remained significant. The inclusion of the CAC-DAD score in a predictive model containing demographic factors and AS improved the C statistic from 0.61 to 0.66 (<i>P</i>=0.008). The fully automated CAC-DAD score improves MACE prediction compared with the AS. Patients with a high CAC-DAD score, including those with a low AS, may be at higher risk and warrant intensification of their preventative therapies.

In many low- and middle-income countries (LMICs), widespread access to obstetric ultrasound is challenged by lack of trained providers, workload, and inadequate resources required for sustainability. Artificial intelligence (AI) is a powerful tool for automating image acquisition and interpretation and may help overcome these barriers. This study explored stakeholders' opinions about how AI-enabled point-of-care ultrasound (POCUS) might change current antenatal care (ANC) services in LMICs and identified key considerations for introduction. We purposely sampled midwives, doctors, researchers, and implementors for this mixed methods study, with a focus on those who live or work in African LMICs. Individuals completed an anonymous web-based survey, then participated in an interview or focus group. Among the 41 participants, we captured demographics, experience with and perceptions of standard POCUS, and reactions to an AI-enabled POCUS prototype description. Qualitative data were analyzed by thematic content analysis and quantitative Likert and rank-order data were aggregated as frequencies; the latter was presented alongside illustrative quotes to highlight overall versus nuanced perceptions. The following themes emerged: (1) priority AI capabilities; (2) potential impact on ANC quality, services and clinical outcomes; (3) health system integration considerations; and (4) research priorities. First, AI-enabled POCUS elicited concerns around algorithmic accuracy and compromised clinical acumen due to over-reliance on AI, but an interest in gestational age automation. Second, there was overall agreement that both standard and AI-enabled POCUS could improve ANC attendance (75%, 65%, respectively), provider-client trust (82%, 60%), and providers' confidence in clinical decision-making (85%, 70%). AI consistently elicited more uncertainty among respondents. Third, health system considerations emerged including task sharing with midwives, ultrasound training delivery and curricular content, and policy-related issues such as data security and liability risks. For both standard and AI-enabled POCUS, clinical decision support and referral strengthening were deemed necessary to improve outcomes. Lastly, ranked priority research areas included algorithm accuracy across diverse populations and impact on ANC performance indicators; mortality indicators were less prioritized. Optimism that AI-enabled POCUS can increase access in settings with limited personnel and resources is coupled with expressions of caution and potential risks that warrant careful consideration and exploration.