To compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. Multi-reader study included 74 consecutive patients (median age = 66 years, [IQR = 57.25-71.75 years]; median PSA = 4.30 ng/mL [IQR = 1.33-7.75 ng/mL]) with suspected or confirmed PCa, who underwent mpMRI between October 2023 and January 2024. The study was conducted in two rounds, separated by a 4-week wash-out period. In each round, four readers evaluated T2W-MRI and standard or AI-generated ADC (AI-ADC) maps. Fleiss' kappa, quadratic-weighted Cohen's kappa statistics were used to assess inter-reader agreement. Linear mixed effect models were employed to compare the quality evaluation of standard versus AI-ADC maps. AI-ADC maps exhibited significantly enhanced imaging quality compared to standard ADC maps with higher ratings in windowing ease (β = 0.67 [95% CI 0.30-1.04], p < 0.05), prostate boundary delineation (β = 1.38 [95% CI 1.03-1.73], p < 0.001), reductions in distortion (β = 1.68 [95% CI 1.30-2.05], p < 0.001), noise (β = 0.56 [95% CI 0.24-0.88], p < 0.001). AI-ADC maps reduced reacquisition requirements for all readers (β = 2.23 [95% CI 1.69-2.76], p < 0.001), supporting potential workflow efficiency gains. No differences were observed between AI-ADC and standard ADC maps' inter-reader agreement. Our multi-reader study demonstrated that AI-ADC maps improved prostate boundary delineation, had lower image noise, fewer distortions, and higher overall image quality compared to ADC maps. Question Can we synthesize apparent diffusion coefficient (ADC) maps with AI to achieve higher quality maps? Findings On average, readers rated quality factors of AI-ADC maps higher than ADC maps in 34.80% of cases, compared to 5.07% for ADC (p < 0.01). Clinical relevance AI-ADC maps may serve as a reliable diagnostic support tool thanks to their high quality, particularly when the acquired ADC maps include artifacts.

Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for successful AI technology implementation into clinical workflows. This study aimed to systematically assess and synthesize the facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging. We conducted a systematic review of 6 medical databases. Using a qualitative content analysis, we extracted the reported facilitators and barriers, outcomes, and moderators in the implementation process of AI. Two reviewers analyzed and categorized the data separately. We then used epistemic network analysis to explore their relationships across different stages of AI implementation. Our search yielded 13,756 records. After screening, we included 38 original studies in our final review. We identified 12 key dimensions and 37 subthemes that influence the implementation of AI in health care workflows. Key dimensions included evaluation of AI use and fit into workflow, with frequency depending considerably on the stage of the implementation process. In total, 20 themes were mentioned as both facilitators and barriers to AI implementation. Studies often focused predominantly on performance metrics over the experiences or outcomes of clinicians. This systematic review provides a thorough synthesis of facilitators and barriers to successful AI implementation in medical imaging. Our study highlights the usefulness of AI technologies in clinical care and the fit of their integration into routine clinical workflows. Most studies did not directly report facilitators and barriers to AI implementation, underscoring the importance of comprehensive reporting to foster knowledge sharing. Our findings reveal a predominant focus on technological aspects of AI adoption in clinical work, highlighting the need for holistic, human-centric consideration to fully leverage the potential of AI in health care. PROSPERO CRD42022303439; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022303439. RR2-10.2196/40485.

To evaluate image quality and diagnostic performance of prostate biparametric MRI (bi-MRI) with deep learning reconstruction (DLR). This prospective study included 61 adult male urological patients undergoing prostate MRI with standard-of-care (SOC) and fast protocols. Sequences included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. DLR images were generated from FAST datasets. Three groups (SOC, FAST, DLR) were compared using: (1) five-point Likert scale, (2) signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), (3) lesion slope profiles, (4) dorsal capsule edge rise distance (ERD). PI-RADS scores were assigned to dominant lesions. ADC values were measured in histopathologically confirmed cases. Diagnostic performance was analyzed via receiver operating characteristic (ROC) curves (accuracy/sensitivity/specificity). Statistical tests included Friedman test, one-way ANOVA with post hoc analyses, and DeLong test for ROC comparisons (P<0.05). FAST scanning protocols reduced acquisition time by nearly half compared to the SOC scanning protocol. When compared to T2WI_FAST, DLR significantly improved SNR, CNR, slope profile, and ERD (P < 0.05). Similarly, DLR significantly enhanced SNR, CNR, and image sharpness when compared to DWI_FAST (P < 0.05). No significant differences were observed in PI-RADS scores and ADC values between groups (P > 0.05). The areas under the ROC curves, sensitivity, and specificity of ADC values for distinguishing benign and malignant lesions remained consistent (P > 0.05). DLR enhances image quality in fast prostate bi-MRI while preserving PI-RADS classification accuracy and ADC diagnostic performance.

Scoliosis affects roughly 2 to 4 percent of adolescents, and treatment decisions depend on precise Cobb angle measurement. Manual assessment is time consuming and subject to inter observer variation. We conducted a retrospective, multi centre evaluation of a fully automated deep learning software (Carebot AI Bones, Spine Measurement functionality; Carebot s.r.o.) on 103 standing anteroposterior whole spine radiographs collected from ten hospitals. Two musculoskeletal radiologists independently measured each study and served as reference readers. Agreement between the AI and each radiologist was assessed with Bland Altman analysis, mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient, and Cohen kappa for four grade severity classification. Against Radiologist 1 the AI achieved an MAE of 3.89 degrees (RMSE 4.77 degrees) with a bias of 0.70 degrees and limits of agreement from minus 8.59 to plus 9.99 degrees. Against Radiologist 2 the AI achieved an MAE of 3.90 degrees (RMSE 5.68 degrees) with a bias of 2.14 degrees and limits from minus 8.23 to plus 12.50 degrees. Pearson correlations were r equals 0.906 and r equals 0.880 (inter reader r equals 0.928), while Cohen kappa for severity grading reached 0.51 and 0.64 (inter reader kappa 0.59). These results demonstrate that the proposed software reproduces expert level Cobb angle measurements and categorical grading across multiple centres, suggesting its utility for streamlining scoliosis reporting and triage in clinical workflows.

Previous studies have confirmed that fully automated three-dimensional echocardiography (3DE) right ventricular (RV) quantification software can accurately assess adult RV function. However, data on its accuracy in children are scarce. This study aimed to test the accuracy of the software in children using cardiac magnetic resonance (MR) as the gold standard. This study prospectively enrolled 82 children who underwent both echocardiography and cardiac MR within 24 h. The RV end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were obtained using the novel 3DE-RV quantification software and compared with cardiac MR values across different groups. The novel 3DE-RV quantification software was feasible in all 82 children (100%). Fully automated analysis was achieved in 35% patients with an analysis time of 8 ± 2 s and 100% reproducibility. Manual editing was necessary in the remaining 65% patients. The 3DE-derived RV volumes and EF correlated well with cardiac MR measurements (RVEDV, r=0.93; RVESV, r=0.90; RVEF, r=0.82; all P <0.001). Although the automated approach slightly underestimated RV volumes and overestimated RVEF compared with cardiac MR in the entire cohort, the bias was smaller in children with RVEF ≥ 45%, normal RV size, and good 3DE image quality. Fully automated 3DE-RV quantification software provided accurate and completely reproducible results in 35% children without any adjustment. The RV volumes and EF measured using the automated 3DE method correlated well with those from cardiac MR, especially in children with RVEF ≥ 45%, normal RV size, and good 3DE image quality. Therefore, the novel automated 3DE method may achieve rapid and accurate assessment of RV function in children with normal heart anatomy.

The prevalence of Alzheimer's disease and other dementias is increasing as populations live longer lifespans. Imaging is becoming a key component of the workup for patients with cognitive impairment or dementia. Integrated PET-MRI provides a unique opportunity for same-session multimodal characterization with many practical benefits to patients, referring physicians, radiologists, and researchers. The impact of integrated PET-MRI on clinical practice for early adopters of this technology can be profound. Classic imaging findings with integrated PET-MRI are illustrated for common neurodegenerative diseases or clinical-radiological syndromes. This review summarizes recent technical innovations that are being introduced into PET-MRI clinical practice and research for neurodegenerative disease. More recent MRI-based attenuation correction now performs similarly compared to PET-CT (e.g., whole-brain bias < 0.5%) such that early concerns for accurate PET tracer quantification with integrated PET-MRI appear resolved. Head motion is common in this patient population. MRI- and PET data-driven motion correction appear ready for routine use and should substantially improve PET-MRI image quality. PET-MRI by definition eliminates ~50% of the radiation from CT. Multiple hardware and software techniques for improving image quality with lower counts are reviewed (including motion correction). These methods can lower radiation to patients (and staff), increase scanner throughput, and generate better temporal resolution for dynamic PET. Deep learning has been broadly applied to PET-MRI. Deep learning analysis of PET and MRI data may provide accurate classification of different stages of Alzheimer's disease or predict progression to dementia. Over the past 5 years, clinical imaging of neurodegenerative disease has changed due to imaging research and the introduction of anti-amyloid immunotherapy-integrated PET-MRI is best suited for imaging these patients and its use appears poised for rapid growth outside academic medical centers. Evidence level: 5. Technical efficacy: Stage 3.

To evaluate image quality and diagnostic interchangeability of synth short-tau inversion recovery (STIR) generated by deep learning in comparison with standard STIR. This prospective study recruited participants between July 2023 and August 2023. Participants were scanned with T1WI and T2WI, then generated Synth-STIR. Signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) were calculated for quantitative evaluation. Four independent, blinded radiologists performed subjective quality and lesion characteristic assessment. Wilcoxon tests were used to assess the differences in SNR, CNR, and subjective image quality. Various diagnostic findings pertinent to the spine were tested for interchangeability using the individual equivalence index (IEI). Inter-reader and intra-reader agreement and concordance were computed, and McNemar tests were performed for comprehensive evaluation. One hundred ninety-nine participants (106 male patients, mean age 46.8 ± 16.9 years) were included. Compared to standard-STIR, Synth-STIR reduces sequence scanning time by approximately 180 s, has significantly higher SNR and CNR (p < 0.001). For artifacts, noise, sharpness, and diagnostic confidence, all readers agreed that Synth-STIR was significantly better than standard-STIR (all p < 0.001). In addition, the IEI was less than 1.61%. Kappa and Kendall showed a moderate to excellent agreement in the range of 0.52-0.97. There was no significant difference in the frequencies of the major features as reported with standard-STIR and Synth-STIR (p = 0.211-1). Synth-STIR shows significantly higher SNR and CNR, and is diagnostically interchangeable with standard-STIR with a substantial overall reduction in the imaging time, thereby improving efficiency without sacrificing diagnostic value. Question Can generating STIR improve image quality while reducing spine MRI acquisition time in order to increase clinical spine MRI throughput? Findings With reduced acquisition time, Synth-STIR has significantly higher SNR and CNR than standard-STIR and can be interchangeably diagnosed with standard-STIR in detecting spinal abnormalities. Clinical relevance Our Synth-STIR provides the same high-quality images for clinical diagnosis as standard-STIR, while reducing scanning time for spine MRI protocols. Increase clinical spine MRI throughput.

Artificial intelligence (AI) is already having a significant impact on healthcare. For example, AI-guided imaging can improve the diagnosis/treatment of vascular diseases, which affect over 200 million people globally. Recently, Chiu and colleagues (2024) developed an AI algorithm that supports nurses with no ultrasound training in diagnosing abdominal aortic aneurysms (AAA) with similar accuracy as ultrasound-trained physicians. This technology can therefore improve AAA screening; however, achieving clinical impact with new AI technologies requires careful consideration of commercialization strategies, including funding, compliance with safety and regulatory frameworks, health technology assessment, regulatory approval, reimbursement, and clinical guideline integration.

Traditional echocardiographic parameters such as ejection fraction (EF) and global longitudinal strain (GLS) have limitations in the early detection of cardiac dysfunction. EF often remains normal despite underlying pathology, and GLS is influenced by load conditions and vendor variability. There is a growing need for reproducible, interpretable, and operator-independent parameters that capture subtle and global cardiac functional alterations. We introduce the Acoustic Index, a novel AI-derived echocardiographic parameter designed to quantify cardiac dysfunction from standard ultrasound views. The model combines Extended Dynamic Mode Decomposition (EDMD) based on Koopman operator theory with a hybrid neural network that incorporates clinical metadata. Spatiotemporal dynamics are extracted from echocardiographic sequences to identify coherent motion patterns. These are weighted via attention mechanisms and fused with clinical data using manifold learning, resulting in a continuous score from 0 (low risk) to 1 (high risk). In a prospective cohort of 736 patients, encompassing various cardiac pathologies and normal controls, the Acoustic Index achieved an area under the curve (AUC) of 0.89 in an independent test set. Cross-validation across five folds confirmed the robustness of the model, showing that both sensitivity and specificity exceeded 0.8 when evaluated on independent data. Threshold-based analysis demonstrated stable trade-offs between sensitivity and specificity, with optimal discrimination near this threshold. The Acoustic Index represents a physics-informed, interpretable AI biomarker for cardiac function. It shows promise as a scalable, vendor-independent tool for early detection, triage, and longitudinal monitoring. Future directions include external validation, longitudinal studies, and adaptation to disease-specific classifiers.

Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump length (CRL) being the gold standard for estimating gestational age and assessing growth during the first trimester. To enhance CRL evaluation accuracy and efficiency, we developed an artificial intelligence (AI)-based model (3DCRL-Net) using the 3D U-Net architecture for automatic landmark detection to achieve CRL plane localization and measurement in 3D ultrasound. We then compared its performance to that of experienced radiologists using both 2D and 3D ultrasound for fetal growth assessment. This prospective consecutive study collected fetal data from 1,326 ultrasound screenings conducted at 11-14 weeks of gestation (June 2021 to June 2023). Three experienced radiologists performed fetal screening using 2D video (2D-RAD) and 3D volume (3D-RAD) to obtain the CRL plane and measurement. The 3DCRL-Net model automatically outputs the landmark position, CRL plane localization and measurement. Three specialists audited the planes achieved by radiologists and 3DCRL-Net as standard or non-standard. The performance of CRL landmark detection, plane localization, measurement and time efficiency was evaluated in the internal testing dataset, comparing results with 3D-RAD. In the external dataset, CRL plane localization, measurement accuracy, and time efficiency were compared among the three groups. The internal dataset consisted of 126 cases in the testing set (training: validation: testing = 8:1:1), and the external dataset included 245 cases. On the internal testing set, 3DCRL-Net achieved a mean absolute distance error of 1.81 mm for the nine landmarks, higher accuracy in standard plane localization compared to 3D-RAD (91.27% vs. 80.16%), and strong consistency in CRL measurements (mean absolute error (MAE): 1.26 mm; mean difference: 0.37 mm, P = 0.70). The average time required per fetal case was 2.02 s for 3DCRL-Net versus 2 min for 3D-RAD (P < 0.001). On the external testing dataset, 3DCRL-Net demonstrated high performance in standard plane localization, achieving results comparable to 2D-RAD and 3D-RAD (accuracy: 91.43% vs. 93.06% vs. 86.12%), with strong consistency in CRL measurements, compared to 2D-RAD, which showed an MAE of 1.58 mm and a mean difference of 1.12 mm (P = 0.25). For 2D-RAD vs. 3DCRL-Net, the Pearson correlation and R² were 0.96 and 0.93, respectively, with an MAE of 0.11 ± 0.12 weeks. The average time required per fetal case was 5 s for 3DCRL-Net, compared to 2 min for 3D-RAD and 35 s for 2D-RAD (P < 0.001). The 3DCRL-Net model provides a rapid, accurate, and fully automated solution for CRL measurement in 3D ultrasound, achieving expert-level performance and significantly improving the efficiency and reliability of first-trimester fetal growth assessment.