Coronary computed tomography angiography (CCTA) is a first-line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA. A hybrid decision-tree with population cohort Markov model was developed from 3393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median (interquartile range) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1214 consecutive patients with extensive guidelines recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 11%, 4%, 4%, and 12% for myocardial infarction, ischaemic stroke, heart failure, and cardiac death, respectively). Implementing AI-Risk Classification in routine interpretation of CCTA is highly likely to be cost-effective (incremental cost-effectiveness ratio £1371-3244), both in scenarios of current guideline compliance, or when applied only to patients without obstructive CAD. Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost-effective, by refining risk-guided medical management.

To investigate the clinical value of deep learning reconstruction (DLR) in accelerated magnetic resonance imaging (MRI) of the knee and compare its visual quality and diagnostic performance metrics with conventional fast spin-echo T2-weighted imaging with fat suppression (FSE-T2WI-FS). This prospective study included 116 patients with knee injuries. All patients underwent both conventional FSE-T2WI-FS and DLR-accelerated FSE-T2WI-FS scans on a 1.5‑T MRI scanner. Two radiologists independently evaluated overall image quality, artifacts, and image sharpness using a 5-point Likert scale. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of lesion regions were measured. Subjective scores were compared using the Wilcoxon signed-rank test, SNR/CNR differences were analyzed via paired t tests, and inter-reader agreement was assessed using Cohen's kappa. The accelerated sequences with DLR achieved a 36 % reduction in total scan time compared to conventional sequences (p < 0.05), shortening acquisition from 9 min 50 s to 6 min 15 s. Moreover, DLR demonstrated superior artifact suppression and enhanced quantitative image quality, with significantly higher SNR and CNR (p < 0.001). Despite these improvements, diagnostic equivalence was maintained: No significant differences were observed in overall image quality, sharpness (p > 0.05), or lesion detection rates. Inter-reader agreement was good (κ> 0.75), further validating the clinical reliability of the DLR technique. Using DLR-accelerated FSE-T2WI-FS reduces scan time, suppresses artifacts, and improves quantitative image quality while maintaining diagnostic accuracy comparable to conventional sequences. This technology holds promise for optimizing clinical workflows in MRI of the knee.

In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity. A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations. Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R² = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm²), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R² = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations. MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

This study considers the application of ultrasound placental image texture analysis for the prediction of hypertensive disorders of pregnancy (HDP) using deep learning (DL) algorithm. In this prospective observational study, placental ultrasound images were taken serially at 11-14 weeks (T1), 20-24 weeks (T2), and 28-32 weeks (T3). Pregnant women with blood pressure at or above 140/90 mmHg on two occasions 4 h apart were considered to have HDP. The image data of women with HDP were compared with those with a normal outcome using DL techniques such as convolutional neural networks (CNN), transfer learning, and a Vision Transformer (ViT) with a TabNet classifier. The accuracy and the Cohen kappa scores of the different DL techniques were compared. A total of 600/1008 (59.5%) subjects had a normal outcome, and 143/1008 (14.2%) had HDP; the reminder, 265/1008 (26.3%), had other adverse outcomes. In the basic CNN model, the accuracy was 81.6% for T1, 80% for T2, and 82.8% for T3. Using the Efficient Net B0 transfer learning model, the accuracy was 87.7%, 85.3%, and 90.3% for T1, T2, and T3, respectively. Using a TabNet classifier with a ViT, the accuracy and area under the receiver operating characteristic curve scores were 91.4% and 0.915 for T1, 90.2% and 0.904 for T2, and 90.3% and 0.907 for T3. The sensitivity and specificity for HDP prediction using ViT were 89.1% and 91.7% for T1, 86.6% and 93.7% for T2, and 85.6% and 94.6% for T3. Ultrasound placental image texture analysis using DL could differentiate women with a normal outcome and those with HDP with excellent accuracy and could open new avenues for research in this field.

To evaluate the feasibility of combining Compressed SENSE (CS) with a newly developed deep learning-based algorithm (CS-AI) using a Convolutional Neural Network to accelerate balanced steady-state free precession (bSSFP)-sequences for cardiac magnetic resonance imaging (MRI). 30 healthy volunteers were examined prospectively with a 3 T MRI scanner. We acquired CINE bSSFP sequences for short axis (SA, multi-breath-hold) and four-chamber (4CH)-view of the heart. For each sequence, four different CS accelerations and CS-AI reconstructions with three different denoising parameters, CS-AI medium, CS-AI strong, and CS-AI complete, were used. Cardiac left ventricular (LV) function (i.e., ejection fraction, end-diastolic volume, end-systolic volume, and LV mass) was analyzed using the SA sequences in every CS factor and each AI level. Two readers, blinded to the acceleration and denoising levels, evaluated all sequences regarding image quality and artifacts using a 5-point Likert scale. Friedman and Dunn's multiple comparison tests were used for qualitative evaluation, ANOVA and Tukey Kramer test for quantitative metrics. Scan time could be decreased up to 57 % for the SA-Sequences and up to 56 % for the 4CH-Sequences compared to the clinically established sequences consisting of SA-CS3 and 4CH-CS2,5 (SA-CS3: 112 s vs. SA-CS6: 48 s; 4CH-CS2,5: 9 s vs. 4CH-CS5: 4 s, p < 0.001). LV-functional analysis was not compromised by using accelerated MRI sequences combined with CS-AI reconstructions (all p > 0.05). The image quality loss and artifact increase accompanying increasing acceleration levels could be entirely compensated by CS-AI post-processing, with the best results for image quality using the combination of the highest CS factor with strong AI (SA-CINE: Coef.:1.31, 95 %CI:1.05-1.58; 4CH-CINE: Coef.:1.18, 95 %CI:1.05-1.58; both p < 0.001), and with complete AI regarding the artifact score (SA-CINE: Coef.:1.33, 95 %CI:1.06-1.60; 4CH-CINE: Coef.:1.31, 95 %CI:0.86-1.77; both p < 0.001). Combining CS sequences with AI-based image reconstruction for denoising significantly decreases scan time in cardiac imaging while upholding LV functional analysis accuracy and delivering stable outcomes for image quality and artifact reduction. This integration presents a promising advancement in cardiac MRI, promising improved efficiency without compromising diagnostic quality.

Body composition analysis (BCA) is a recognized indicator of patient frailty. Apart from the established bioelectrical impedance analysis (BIA), computed tomography (CT)-derived BCA is being increasingly explored. The aim of this prospective study was to directly compare BCA obtained from BIA and CT. A total of 210 consecutive patients scheduled for CT, including a high proportion of cancer patients, were prospectively enrolled. Immediately prior to the CT scan, all patients underwent BIA. CT-based BCA was performed using a single-slice AI tool for automated detection and segmentation at the level of the third lumbar vertebra (L3). BIA-based parameters, body fat mass (BFM_BIA) and skeletal muscle mass (SMM_BIA), CT-based parameters, subcutaneous and visceral adipose tissue area (SATA_CT and VATA_CT) and total abdominal muscle area (TAMA_CT) were determined. Indices were calculated by normalizing the BIA and CT parameters to patient's weight (body fat percentage (BFP_BIA) and body fat index (BFI_CT)) or height (skeletal muscle index (SMI_BIA) and lumbar skeletal muscle index (LSMI_CT)). Parameters representing fat, BFM_BIA and SATA_CT + VATA_CT, and parameters representing muscle tissue, SMM_BIA and TAMA_CT, showed strong correlations in female (fat: r = 0.95; muscle: r = 0.72; p < 0.001) and male (fat: r = 0.91; muscle: r = 0.71; p < 0.001) patients. Linear regression analysis was statistically significant (fat: R² = 0.73 (female) and 0.74 (male); muscle: R² = 0.56 (female) and 0.56 (male); p < 0.001), showing that BFI_CT and LSMI_CT allowed prediction of BFP_BIA and SMI_BIA for both sexes. CT-based BCA strongly correlates with BIA results and yields quantitative results for BFP and SMI comparable to the existing gold standard. Question CT-based body composition analysis (BCA) is moving more and more into clinical focus, but validation against established methods is lacking. Findings Fully automated CT-based BCA correlates very strongly with guideline-accepted bioelectrical impedance analysis (BIA). Clinical relevance BCA is currently moving further into clinical focus to improve assessment of patient frailty and individualize therapies accordingly. Comparability with established BIA strengthens the value of CT-based BCA and supports its translation into clinical routine.

The aim of this study was to guide prostatectomy by employing artificial intelligence for the segmentation of tumor gross tumor volume (GTV) and neurovascular bundles (NVB). The preservation and dissection of NVB differ between intrafascial and extrafascial robot-assisted radical prostatectomy (RARP), impacting postoperative urinary control. We trained the nnU-Net v2 neural network using data from 220 patients in the PI-CAI cohort for the segmentation of prostate GTV and NVB in biparametric magnetic resonance imaging (bpMRI). The model was then validated in an external cohort of 209 patients from Renmin Hospital of Wuhan University (RHWU). Utilizing three-dimensional reconstruction and point cloud analysis, we explored the spatial distribution of GTV and NVB in relation to intrafascial and extrafascial approaches. We also prospectively included 40 patients undergoing intrafascial and extrafascial RARP, applying the aforementioned procedure to classify the surgical approach. Additionally, 3D printing was employed to guide surgery, and follow-ups on short- and long-term urinary function in patients were conducted. The nnU-Net v2 neural network demonstrated precise segmentation of GTV, NVB, and prostate, achieving Dice scores of 0.5573 ± 0.0428, 0.7679 ± 0.0178, and 0.7483 ± 0.0290, respectively. By establishing the distance from GTV to NVB, we successfully predicted the surgical approach. Urinary control analysis revealed that the extrafascial approach yielded better postoperative urinary function, facilitating more refined management of patients with prostate cancer and personalized medical care. Artificial intelligence technology can accurately identify GTV and NVB in preoperative bpMRI of patients with prostate cancer and guide the choice between intrafascial and extrafascial RARP. Patients undergoing intrafascial RARP with preserved NVB demonstrate improved postoperative urinary control.

The Varian Ethos system allows not only on-treatment-table plan adaptation but also automated contouring with the aid of artificial intelligence. This study evaluates the initial clinical implementation of an adaptive pelvic radiotherapy system, focusing on the treatment times and the associated learning curve. We analyzed the data from 903 consecutive treatments for most urogenital cancers at our center. The treatment time was calculated from the time of the first cone-beam computed tomography scan used for replanning until the end of treatment. To calculate whether treatments were generally shorter over time, we divided the date of the first treatment into 3-months quartiles. Differences between the groups were calculated using t-tests. The mean time from the first cone-beam computed tomography scan to the end of treatment was 25.9min (standard deviation: 6.9min). Treatment time depended on the number of planning target volumes and treatment of the pelvic lymph nodes. The mean time from cone-beam computed tomography to the end of treatment was 37 % longer if the pelvic lymph nodes were treated and 26 % longer if there were more than two planning target volumes. There was a learning curve: in linear regression analysis, both quartiles of months of treatment (odds ratio [OR]: 1.3, 95 % confidence interval [CI]: 1.8-0.70, P<0.001) and the number of planning target volumes (OR: 3.0, 95 % CI: 2.6-3.4, P<0.001) were predictive of treatment time. Approximately two-thirds of the treatments were delivered within 33min. Treatment time was strongly dependent on the number of separate planning target volumes. There was a continuous learning curve.

The new artificial intelligence-based software, Roadmap (HeartFlow), may assist in evaluating coronary artery stenosis during cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Consecutive TAVR candidates who underwent both cardiac CT angiography (CTA) and invasive coronary angiography were enrolled. We evaluated the ability of three methods to predict obstructive coronary artery disease (CAD), defined as ≥50 ​% stenosis on quantitative coronary angiography (QCA), and the need for percutaneous coronary intervention (PCI) within one year: Roadmap, clinician CT specialists with Roadmap, and CT specialists alone. The area under the curve (AUC) for predicting QCA ≥50 ​% stenosis was similar for CT specialists with or without Roadmap (0.93 [0.85-0.97] vs. 0.94 [0.88-0.98], p ​= ​0.82), both significantly higher than Roadmap alone (all p ​< ​0.05). For PCI prediction, no significant differences were found between QCA and CT specialists, with or without Roadmap, while Roadmap's AUC was lower (all p ​< ​0.05). The negative predictive value (NPV) of CT specialists with Roadmap for ≥50 ​% stenosis was 97 ​%, and for PCI prediction, the NPV was comparable to QCA (p ​= ​1.00). In contrast, the positive predictive value (PPV) of Roadmap alone for ≥50 ​% stenosis was 49 ​%, the lowest among all approaches, with a similar trend observed for PCI prediction. While Roadmap alone is insufficient for clinical decision-making due to low PPV, Roadmap may serve as a "second observer", providing a supportive tool for CT specialists by flagging lesions for careful review, thereby enhancing workflow efficiency and maintaining high diagnostic accuracy with excellent NPV.

We conducted a prospective study to evaluate the usefulness of ultralow-dose computed tomography (ULD-CT) with deep-learning reconstruction (DLR) compared with conventional standard-dose CT (SD-CT) for post-endovascular aneurysm repair (EVAR) surveillance. We prospectively performed post-EVAR surveillance using ULD-CT at a single center in 44 patients after they had received SD-CT. The ULD-CT images underwent DLR, whereas the SD-CT images underwent iterative reconstruction. Three radiologists blinded to the patient information and CT conditions independently measured the aneurysmal sac diameter and evaluated the overall image quality. Bland-Altman analysis and a linear mixed-effects model were used to assess and compare the measurement accuracy between SD-CT and ULD-CT. The mean CT dose index volume and dose-length product were significantly lower for ULD-CT (1.0 ± 0.3 mGy and 71.4 ± 26.5 mGy•cm) than that for SD-CT (6.9 ± 0.9 mGy and 500.9 ± 96.0 mGy•cm; p<0.001). The mean short diameters of the aneurysmal sac measured by the 3 observers were 46.7 ± 10.8 mm on SD-CT and 46.3 ± 10.8 mm on ULD-CT. The mean difference in the short diameter of the aneurysmal sac between ULD-CT and SD-CT was -0.37 mm (95% confidence interval, -0.6 to -0.12 mm). The intraobserver limits of agreement (LOA) for measurements by ULD-CT and SD-CT were -3.5 to 2.6, -2.8 to 1.9, and -2.9 to 2.3 for Observers 1, 2, and 3, respectively. The pairwise LOAs for assessing interobserver agreement, such as for the differences between Observers 1 and 2 measurements in SD-CT, were mostly within the predetermined acceptable range. The mean image-quality score was lower for ULD-CT (3.3 ± 0.6) than that for SD-CT (4.5 ± 0.5; p<0.001). Aneurysmal sac diameter measurements by ULD-CT with DLR were sufficiently accurate for post-EVAR surveillance, with substantial radiation reduction versus SD-CT.Clinical ImpactDeep-learning reconstruction (DLR) is implemented as a software-based algorithm rather than requiring dedicated hardware. As such, it is expected to be integrated into standard computed tomography (CT) systems in the near future. The ultralow-dose CT (ULD-CT) with DLR evaluated in this study has the potential to become widely accessible across various institutions. This advancement could substantially reduce radiation exposure in post-endovascular aneurysm repair (EVAR) CT imaging, thereby facilitating its adoption as a standard modality for post-EVAR surveillance.