Multiple sclerosis (MS) diagnosis and monitoring rely heavily on accurate assessment of brain MRI biomarkers, particularly white matter hyperintensities (WMHs) and ventricular changes. Current segmentation approaches suffer from several limitations: they typically segment these structures independently despite their pathophysiological relationship, struggle to differentiate between normal and pathological hyperintensities, and are poorly optimized for anisotropic clinical MRI data. We propose a novel 2D pix2pix-based deep learning framework for simultaneous segmentation of ventricles and WMHs with the unique capability to distinguish between normal periventricular hyperintensities and pathological MS lesions. Our method was developed and validated on FLAIR MRI scans from 300 MS patients. Compared to established methods (SynthSeg, Atlas Matching, BIANCA, LST-LPA, LST-LGA, and WMH-SynthSeg), our approach achieved superior performance for both ventricle segmentation (Dice: 0.801+/-0.025, HD95: 18.46+/-7.1mm) and WMH segmentation (Dice: 0.624+/-0.061, precision: 0.755+/-0.161). Furthermore, our method successfully differentiated between normal and abnormal hyperintensities with a Dice coefficient of 0.647. Notably, our approach demonstrated exceptional computational efficiency, completing end-to-end processing in approximately 4 seconds per case, up to 36 times faster than baseline methods, while maintaining minimal resource requirements. This combination of improved accuracy, clinically relevant differentiation capability, and computational efficiency addresses critical limitations in current neuroimaging analysis, potentially enabling integration into routine clinical workflows and enhancing MS diagnosis and monitoring.

Transcatheter heart valve (THV) durability is a critical concern, and its deformation may influence long-term performance. Current assessments rely on CT-based single-phase measurements and require a tedious analysis process, potentially overlooking deformation dynamics throughout the cardiac cycle. A fully automated artificial intelligence-based method was developed to assess THV deformation in post-transcatheter aortic valve implantation (TAVI) 4DCT scans. The approach involves segmenting the THV, extracting orthogonal cross-sections along its axis, fitting ellipses to these cross-sections, and computing eccentricity to analyze deformation over the cardiac cycle. The method was evaluated in 21 TAVI patients with different self-expandable THV models, using one post-TAVI 4DCT series per patient. The THV inflow level exhibited the greatest eccentricity variations (0.35-0.69 among patients with the same THV model at end-diastole). Additionally, eccentricity varied throughout the cardiac cycle (0.23-0.57), highlighting the limitations of single-phase assessments in characterizing THV deformation. This method enables automated THV deformation assessment based on cross-sectional eccentricity. Significant differences were observed at the inflow level, and cyclic variations suggest that full cardiac cycle analysis provides a more comprehensive evaluation than single-phase measurements. This approach may aid in optimizing THV durability and function while preventing related complications.

Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for inadequate acetabular coverage. To determine the differences in labral contribution to the joint surface among different hip deformities as well as which radiographic parameters influence labral contribution to the joint surface using a deep learning-based approach for automatic 3-dimensional (3D) segmentation of MRI. Cross-sectional study; Level of evidence, 4. This retrospective study was approved by the local ethics committee with waiver for informed consent. A total of 98 patients (100 hips) with symptomatic hip deformities undergoing direct hip magnetic resonance arthrography (3 T) between January 2020 and October 2021 were consecutively selected (mean age, 30 ± 9 years; 64% female). The standard imaging protocol included proton density-weighted turbo spin echo images and an axial-oblique 3D T1-weighted MP2RAGE sequence. According to acetabular morphology, hips were divided into subgroups: dysplasia (lateral center-edge [LCE] angle, <23°), normal coverage (LCE, 23°-33°), overcoverage (LCE, 33°-39°), severe overcoverage (LCE, >39°), and retroversion (retroversion index >10% and all 3 retroversion signs positive). A previously validated deep learning approach for automatic segmentation and software for calculation of the joint surface were used. The labral contribution to the joint surface was defined as follows: labrum surface area/(labrum surface area + cartilage surface area). One-way analysis of variance with Tukey correction for multiple comparison and linear regression analysis was performed. The mean labral contribution of the joint surface of dysplastic hips was 26% ± 5% (95% CI, 24%-28%) and higher compared with all other hip deformities (<i>P</i> value range, .001-.036). Linear regression analysis identified LCE angle (β = -.002; <i>P</i> < .001) and femoral torsion (β = .001; <i>P</i> = .008) as independent predictors for labral contribution to the joint surface with a goodness-of-fit <i>R</i>² value of 0.35. The labral contribution to the joint surface differs among hip deformities and is influenced by lateral acetabular coverage and femoral torsion. This study paves the way for a more in-depth understanding of the underlying pathomechanism and a reliable 3D analysis of the hip joint that can be indicative for surgical decision-making in patients with hip deformities.

Skull damage caused by craniectomy or trauma necessitates accurate and precise Patient-Specific Implant (PSI) design to restore the cranial cavity. Conventional Computer-Aided Design (CAD)-based methods for PSI design are highly infrastructure-intensive, require specialised skills, and are time-consuming, resulting in prolonged patient wait times. Recent advancements in Artificial Intelligence (AI) provide automated, faster and scalable alternatives. This study introduces the Skull Completion using AI Network (SCAI-Net) framework, a deep-learning-based approach for automated cranial defect reconstruction using Computer Tomography (CT) images. The framework proposes two defect reconstruction variants: SCAI-Net-SDR (Subtraction-based Defect Reconstruction), which first reconstructs the full skull, then performs binary subtraction to obtain the reconstructed defect, and SCAI-Net-DDR (Direct Defect Reconstruction), which generates the reconstructed defect directly without requiring full-skull reconstruction. To enhance model robustness, the SCAI-Net was trained on an augmented dataset of 2760 images, created by combining MUG500+ and SkullFix datasets, featuring artificial defects across multiple cranial regions. Unlike subtraction-based SCAI-Net-SDR, which requires full-skull reconstruction before binary subtraction, and conventional CAD-based methods, which rely on interpolation or mirroring, SCAI-Net-DDR significantly reduces computational overhead. By eliminating the full-skull reconstruction step, DDR reduces training time by 66 % (85 min vs. 250 min for SDR) and achieves a 99.996 % faster defect reconstruction time compared to CAD (0.1s vs. 2400s). Based on the quantitative evaluation conducted on the SkullFix test cases, SCAI-Net-DDR emerged as the leading model among all evaluated approaches. SCAI-Net-DDR achieved the highest Dice Similarity Coefficient (DSC: 0.889), a low Hausdorff Distance (HD: 1.856 mm), and a superior Structural Similarity Index (SSIM: 0.897). Similarly, within the subset of subtraction-based reconstruction approaches evaluated, SCAI-Net-SDR demonstrated competitive performance, achieving the best HD (1.855 mm) and the highest SSIM (0.889), confirming its strong standing among methods using the subtraction paradigm. SCAI-Net generates reconstructed defects, which undergo post-processing to ensure manufacturing readiness. Steps include surface smoothing, thickness validation and edge preparation for secure fixation and seamless digital manufacturing compatibility. End-to-end implant generation time for DDR demonstrated a 96.68 % reduction (93.5 s), while SDR achieved a 96.64 % reduction (94.6 s), significantly outperforming CAD-based methods (2820s). Finite Element Analysis (FEA) confirmed the SCAI-Net-generated implants' robust load-bearing capacity under extreme loading (1780N) conditions, while edge gap analysis validated precise anatomical fit. Clinical validation further confirmed boundary accuracy, curvature alignment, and secure fit within cranial cavity. These results position SCAI-Net as a transformative, time-efficient, and resource-optimized solution for AI-driven cranial defect reconstruction and implant generation.

Identification of the mandibular incisive canal (MIC) prior to anterior implant placement is often challenging. The present study aimed to validate an enhanced artificial intelligence (AI)-driven model dedicated to automated segmentation of MIC on cone beam computed tomography (CBCT) scans and to compare its accuracy and time efficiency with simultaneous segmentation of both mandibular canal (MC) and MIC by either human experts or a previously trained AI model. An enhanced AI model was developed based on 100 CBCT scans using expert-optimized MIC segmentation within the Virtual Patient Creator platform. The performance of the enhanced AI model was tested against human experts and a previously trained AI model using another 40 CBCT scans. Performance metrics included intersection over union (IoU), dice similarity coefficient (DSC), recall, precision, accuracy, and root mean square error (RSME). Time efficiency was also evaluated. The enhanced AI model had IoU of 93%, DSC of 93%, recall of 94%, precision of 93%, accuracy of 99%, and RMSE of 0.23 mm. These values were significantly higher than those of the previously trained AI model for all metrics, and for manual segmentation for IoU, DSC, recall, and accuracy (p < 0.0001). The enhanced AI model demonstrated significant time efficiency, completing segmentation in 17.6 s (125 times faster than manual segmentation) (p < 0.0001). The enhanced AI model proved to allow a unique and accurate automated MIC segmentation with high accuracy and time efficiency. Besides, its performance was superior to human expert segmentation and a previously trained AI model segmentation.

Mandibular reconstruction to restore mandibular continuity often relies on patient-specific implants and virtual surgical planning, but current implant designs rarely consider individual biomechanical demands, which are critical for preventing complications such as stress shielding, screw loosening, and implant failure. The inclusion of patient-specific masticatory muscle parameters such as cross-sectional area, vectors, and volume could improve implant success, but manual segmentation of these parameters is time-consuming, limiting large-scale analyses. In this study, a deep learning model was trained for automatic segmentation of eight masticatory muscles on MRI images. Forty T1-weighted MRI scans were segmented manually or via pseudo-labelling for training. Training employed 5-fold cross-validation over 1000 epochs per fold and testing was done on 10 manually segmented scans. The model achieved a mean Dice similarity coefficient (DSC) of 0.88, intersection over union (IoU) of 0.79, precision of 0.87, and recall of 0.89, demonstrating high segmentation accuracy. These results indicate the feasibility of large-scale, reproducible analyses of muscle volumes, directions, and estimated forces. By integrating these parameters into implant design and surgical planning, this method offers a step forward in developing personalized surgical strategies that could improve postoperative outcomes in mandibular reconstruction. This brings the field closer to truly individualized patient care.

Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data. This retrospective study collected CT perfusion data at admission for AIS patients receiving MT over 6 hours after symptom onset, from January 2020 to December 2024, across three centers. Infarct on post-MT diffusion weighted imaging served as ground truth. Five Swin transformer based models were developed for post-MT infarct segmentation using pre-MT CT perfusion parameter maps: BaselineNet served as the basic model for comparative analysis, CollateralFlowNet included a collateral circulation evaluation score, InfarctProbabilityNet incorporated infarct probability mapping, ArterialTerritoryNet was guided by artery territory mapping, and UnifiedNet combined all prior knowledge sources. Model performance was evaluated using the Dice coefficient and intersection over union (IoU). A total of 221 patients with AIS were included (65.2% women) with a median age of 73 years. Baseline ischemic core based on CT perfusion threshold achieved a Dice coefficient of 0.50 and IoU of 0.33. BaselineNet improved to a Dice coefficient of 0.69 and IoU of 0.53. Compared with BaselineNet, models incorporating medical knowledge demonstrated higher performance: CollateralFlowNet (Dice coefficient 0.72, IoU 0.56), InfarctProbabilityNet (Dice coefficient 0.74, IoU 0.58), ArterialTerritoryNet (Dice coefficient 0.75, IoU 0.60), and UnifiedNet (Dice coefficient 0.82, IoU 0.71) (all P<0.05). In this study, integrating medical knowledge into deep learning models enhanced the accuracy of infarct predictions in AIS patients undergoing extended time window MT.

Body mass index (BMI) does not account for substantial inter-individual differences in regional fat and muscle compartments, which are relevant for the prevalence of cardiometabolic and cancer conditions. We applied a validated deep learning pipeline for automated segmentation of whole-body MRI scans in 45,851 adults from the UK Biobank and German National Cohort, enabling harmonized quantification of visceral (VAT), gluteofemoral (GFAT), and abdominal subcutaneous adipose tissue (ASAT), liver fat fraction (LFF), and trunk muscle volume. Associations with clinical conditions were evaluated using compartment measures adjusted for age, sex, height, and BMI. Our analysis demonstrates that regional adiposity and muscle volume show distinct associations with cardiometabolic and cancer prevalence, and that substantial disease heterogeneity exists within BMI strata. The analytic framework and reference data presented here will support future risk stratification efforts and facilitate the integration of automated MRI phenotyping into large-scale population and clinical research.

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.

Ultrasound-guided transverse thoracic plane (TTP) block has been shown to be highly effective in relieving postoperative pain in a variety of surgeries involving the anterior chest wall. Accurate identification of the target structure on ultrasound images is key to the successful implementation of TTP block. Nevertheless, the complexity of anatomical structures in the targeted blockade area coupled with the potential for adverse clinical incidents presents considerable challenges, particularly for anesthesiologists who are less experienced. This study applied deep learning methods to TTP block and developed a deep learning model to achieve real-time region segmentation in ultrasound to assist doctors in the accurate identification of the target nerve. Using 2329 images from 155 patients, we successfully segmented key structures associated with TTP areas and nerve blocks, including the transversus thoracis muscle, lungs, and bones. The achieved IoU (Intersection over Union) scores are 0.7272, 0.9736, and 0.8244 in that order. Recall metrics were 0.8305, 0.9896, and 0.9336 respectively, whilst Dice coefficients reached 0.8421, 0.9866, and 0.9037, particularly with an accuracy surpassing 97% in the identification of perilous lung regions. The real-time segmentation frame rate of the model for ultrasound video was as high as 42.7 fps, thus meeting the exigencies of performing nerve blocks under real-time ultrasound guidance in clinical practice. This study introduces TTP-Unet, a deep learning model specifically designed for TTP block, capable of automatically identifying crucial anatomical structures within ultrasound images of TTP block, thereby offering a practicable solution to attenuate the clinical difficulty associated with TTP block technique.