Neuromodulation of the centromedian nucleus (CM) of the thalamus has shown promise in treating refractory epilepsy, particularly for idiopathic generalized epilepsy and Lennox-Gastaut syndrome. However, precise targeting of CM remains challenging. The combination of deep learning reconstruction (DLR) and fast gray matter acquisition T1 inversion recovery (FGATIR) offers potential improvements in visualization of CM for deep brain stimulation (DBS) targeting. The goal of the study was to evaluate the visualization of the putative CM on DLR-FGATIR and its alignment with atlas-defined CM boundaries, with the aim of facilitating direct targeting of CM for neuromodulation. This retrospective study included 12 patients with drug-resistant epilepsy treated with thalamic neuromodulation by using DLR-FGATIR for direct targeting. Postcontrast-T1-weighted MRI, DLR-FGATIR, and postoperative CT were coregistered and normalized into Montreal Neurological Institute (MNI) space and compared with the Morel histologic atlas. Contrast-to-noise ratios were measured between CM and neighboring nuclei. CM segmentations were compared between an experienced rater, a trainee rater, the Morel atlas, and the Thalamus Optimized Multi Atlas Segmentation (THOMAS) atlas (derived from expert segmentation of high-field MRI) by using the Sorenson-Dice coefficient (Dice score, a measure of overlap) and volume ratios. The number of electrode contacts within the Morel atlas CM was assessed. On DLR-FGATIR, CM was visible as an ovoid hypointensity in the intralaminar thalamus. Contrast-to-noise ratios were highest (<i>P</i> < .001) for the mediodorsal and medial pulvinar nuclei. Dice score with the Morel atlas CM was higher (median 0.49, interquartile range 0.40-0.58) for the experienced rater (<i>P</i> < .001) than the trainee rater (0.32, 0.19-0.46) and no different (<i>P</i> = .32) than the THOMAS atlas CM (0.56, 0.55-0.58). Both raters and the THOMAS atlas tended to under-segment the lateral portion of the Morel atlas CM, reflected by smaller segmentation volumes (<i>P</i> < .001). All electrodes targeting CM based on DLR-FGATIR traversed the Morel atlas CM. DLR-FGATIR permitted visualization and delineation of CM commensurate with a group atlas derived from high-field MRI. This technique provided reliable guidance for accurate electrode placement within CM, highlighting its potential use for direct targeting.

Hand and foot digital radiography (DR) is an indispensable tool in medical imaging, with varying diagnostic requirements necessitating different hand and foot positionings. Accurate positioning is crucial for obtaining diagnostically valuable images. Furthermore, adjusting exposure parameters such as exposure area based on patient conditions helps minimize the likelihood of image retakes. We propose a personalized positioning and exposure assistant capable of automatically recognizing hand and foot positionings and recommending appropriate exposure parameters to achieve these objectives. The assistant comprises three modules: (1) Progressive Iterative Hand-Foot Tracker (PIHFT) to iteratively locate hands or feet in RGB images, providing the foundation for accurate pose estimation; (2) Multi-Task Shared Pose Estimation Transformer (MTSPET), a Transformer-based model that encompasses hand and foot estimation branches with similar network architectures, sharing a common backbone. MTSPET outperformed MediaPipe in the hand pose estimation task and successfully transferred this capability to the foot pose estimation task; (3) Domain Expertise-embedded Positioning and Exposure Assistant (DEPEA), which combines the key-point coordinates of hands and feet with specific positioning and exposure parameter requirements, capable of checking patient positioning and inferring exposure areas and Regions of Interest (ROIs) of Digital Automatic Exposure Control (DAEC). Additionally, two datasets were collected and used to train MTSPET. A preliminary clinical trial showed strong agreement between PPEA's outputs and manual annotations, indicating the system's effectiveness in typical clinical scenarios. The contributions of this study lay the foundation for personalized, patient-specific imaging strategies, ultimately enhancing diagnostic outcomes and minimizing the risk of errors in clinical settings.

This study aimed to explore the diagnostic performance of ultrasound S-Detect in differentiating Breast Imaging-Reporting and Data System (BI-RADS) 4 breast nodules ≤ 20 mm and > 20 mm. Between November 2020 and November 2022, a total of 382 breast nodules in 312 patients were classified as BI-RADS-4 by conventional ultrasound. Using pathology results as the gold standard, we applied receiver operator characteristics (ROC), sensitivity (SE), specificity (SP), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) to analyze the diagnostic value of BI-RADS, S-Detect, and the two techniques in combination (Co-Detect) in the diagnosis of BI-RADS 4 breast nodules ≤ 20 mm and > 20 mm. There were 382 BI-RADS-4 nodules, of which 151 were pathologically confirmed as malignant, and 231 as benign. In lesions ≤ 20 mm, the SE, SP, ACC, PPV, NPV, and area under the curve (AUC) of the BI-RADS group were 77.27%, 89.73%, 85.71%, 78.16%, 89.24%, 0.835, respectively. SE, SP, ACC, PPV, NPV, and AUC of the S-Detect group were 92.05%, 78.92%, 83.15%, 67.50%, 95.43%, 0.855, respectively. SE, SP, ACC, PPV, NPV, and AUC of the Co-Detect group were 89.77%, 93.51%, 92.31%, 86.81%, 95.05%, 0.916, respectively. The differences of SE, ACC, NPV, and AUC between the BI-RADS group and the Co-Detect group were statistically significant (P < 0.05). In lesions > 20 mm, SE, SP, ACC, PPV, NPV, and AUC of the BI-RADS group were 88.99%, 89.13%, 88.99%, 91.80%, 85.42%, 0.890, respectively. SE, SP, ACC, PPV, NPV, and AUC of the S-Detect group were 98.41%, 69.57%, 86.24%, 81.58%, 96.97%, 0.840, respectively. SE, SP, ACC, PPV, NPV, and AUC of the Co-Detect group were 98.41%, 91.30%, 95.41%, 93.94%, 97.67%, 0.949, respectively. A total of 166 BI-RADS 4 A nodules were downgraded to category 3 by Co-Detect, with 160 (96.4%) confirmed as benign and 6 (all ≤ 20 mm) as false negatives. Conversely, 25 nodules were upgraded to 4B, of which 19 (76.0%) were malignant. The difference in AUC between the BI-RADS group and the Co-Detect group was statistically significant (P < 0.05). S-Detect combined with BI-RADS is effective in the differential diagnosis of BI-RADS 4 breast nodules ≤ 20 mm and > 20 mm. However, its performance is particularly pronounced in lesions ≤ 20 mm, where it contributes to a significant reduction in unnecessary biopsies.

Polycystic ovary syndrome (PCOS) has a significant impact on endocrine metabolism, reproductive function, and mental health in women of reproductive age. Ultrasound remains an essential diagnostic tool for PCOS, particularly in individuals presenting with oligomenorrhea or ovulatory dysfunction accompanied by polycystic ovaries, as well as hyperandrogenism associated with polycystic ovaries. However, the accuracy of ultrasound in identifying polycystic ovarian morphology remains variable. To develop a deep learning model capable of rapidly and accurately identifying PCOS using ovarian ultrasound images. Prospective diagnostic accuracy study. This prospective study included data from 1,751 women with suspected PCOS who presented at two affiliated hospitals at Central South University, with clinical and ultrasound information collected and archived. Patients from center 1 were randomly divided into a training set and an internal validation set in a 7:3 ratio, while patients from center 2 served as the external validation set. Using the YOLOv11 deep learning framework, an automated recognition model for ovarian ultrasound images in PCOS cases was constructed, and its diagnostic performance was evaluated. Ultrasound images from 933 patients (781 from center 1 and 152 from center 2) were analyzed. The mean average precision of the YOLOv11 model in detecting the target ovary was 95.7%, 97.6%, and 97.8% for the training, internal validation, and external validation sets, respectively. For diagnostic classification, the model achieved an F1 score of 95.0% in the training set and 96.9% in both validation sets. The area under the curve values were 0.953, 0.973, and 0.967 for the training, internal validation, and external validation sets respectively. The model also demonstrated significantly faster evaluation of a single ovary compared to clinicians (doctor, 5.0 seconds; model, 0.1 seconds; <i>p</i> < 0.01). The YOLOv11-based automatic recognition model for PCOS ovarian ultrasound images exhibits strong target detection and diagnostic performance. This approach can streamline the follicle counting process in conventional ultrasound and enhance the efficiency and generalizability of ultrasound-based PCOS assessment.

Accurate thyroid volume assessment is critical in thyroid disease diagnostics, yet conventional high-resolution 2D ultrasound has limitations. Freehand 3D ultrasound with AI-assisted segmentation presents a potential advancement, but its clinical accuracy requires validation. This prospective clinical trial included 14 patients scheduled for total thyroidectomy. Preoperative thyroid volume was measured using both 2D ultrasound (ellipsoid method) and freehand 3D ultrasound with AI segmentation. Postoperative thyroid volume, determined via the water displacement method, served as the reference standard. The median postoperative thyroid volume was 14.8 mL (IQR 8.8-20.2). The median volume difference was 1.7 mL (IQR 1.2-3.3) for 3D ultrasound and 3.6 mL (IQR 2.3-6.6) for 2D ultrasound (p = 0.02). The inter-operator reliability coefficient for 3D ultrasound was 0.986 (p < 0.001). These findings suggest that freehand 3D ultrasound with AI-assisted segmentation provides superior accuracy and reproducibility compared to 2D ultrasound and may enhance clinical thyroid volume assessment. ClinicalTrials.gov identifier: NCT05510609.

AI-based automatic contouring streamlines radiotherapy by reducing contouring time but requires rigorous validation and ongoing daily monitoring. This study assessed how software updates affect contouring accuracy and examined how image quality variations influence AI performance. Two patient cohorts were analyzed. The software updates cohort (40 CT scans: 20 thorax, 10 pelvis, 10 H&N) compared six versions of Limbus AI contouring software. The image quality cohort (20 patients: H&N, pelvis, brain, thorax) analyzed 12 reconstructions per patient using Standard, iDose, and IMR algorithms, with simulated noise and spatial resolution (SR) degradations. AI performance was assessed using Volumetric Dice Similarity Coefficient (vDSC) and 95 % Hausdorff Distance (HD95%) with Wilcoxon tests for significance. In the software updates cohort, vDSC improved for re-trained structures across versions (mean DSC ≥ 0.75), with breast contour vDSC decreasing by 1 % between v1.5 and v1.8B3 (p > 0.05). Median HD95% values were consistently <4 mm, <5 mm, and <12 mm for H&N, pelvis, and thorax contours, respectively (p > 0.05). In the image quality cohort, no significant differences were observed between Standard, iDose, and IMR algorithms. However, noise and SR degradation significantly reduced performance: vDSC ≥ 0.9 dropped from 89 % at 2 % noise to 30 % at 20 %, and from 87 % to 70 % as SR degradation increased (p < 0.001). AI contouring accuracy improved with software updates and showed robustness to minor reconstruction variations, but it was sensitive to noise and SR degradation. Continuous validation and quality control of AI-generated contours are essential. Future studies should include a broader range of anatomical regions and larger cohorts.

To validate a deep learning (DL) reconstruction technique for faster post-contrast enhanced coronal Volume Interpolated Breath-hold Examination (VIBE) sequences and assess its image quality compared to conventionally acquired coronal VIBE sequences. This prospective study included 151 patients undergoing clinically indicated upper abdominal MRI acquired on 3 T scanners. Two coronal T1 fat-suppressed VIBE sequences were acquired: a DL-reconstructed sequence (VIBE_DL) and a standard sequence (VIBE_SD). Three radiologists independently evaluated six image quality parameters: overall image quality, perceived signal-to-noise ratio, severity of artifacts, liver edge sharpness, liver vessel sharpness, and lesion conspicuity, using a 4-point Likert scale. Inter-reader agreement was assessed using Gwet's AC2. Ordinal mixed-effects regression models were used to compare VIBE_DL and VIBE_SD. Acquisition times were 10.2 s for VIBE_DL compared to 22.3 s for VIBE_SD. VIBE_DL demonstrated superior overall image quality (OR 1.95, 95 % CI: 1.44-2.65, p < 0.001), reduced image noise (OR 3.02, 95 % CI: 2.26-4.05, p < 0.001), enhanced liver edge sharpness (OR 3.68, 95 % CI: 2.63-5.15, p < 0.001), improved liver vessel sharpness (OR 4.43, 95 % CI: 3.13-6.27, p < 0.001), and better lesion conspicuity (OR 9.03, 95 % CI: 6.34-12.85, p < 0.001) compared to VIBE_SD. However, VIBE_DL showed increased severity of peripheral artifacts (OR 0.13, p < 0.001). VIBE_DL detected 137/138 (99.3 %) focal liver lesions, while VIBE_SD detected 131/138 (94.9 %). Inter-reader agreement ranged from good to very good for both sequences. The DL-reconstructed VIBE sequence significantly outperformed the standard breath-hold VIBE in image quality and lesion detection, while reducing acquisition time. This technique shows promise for enhancing the diagnostic capabilities of contrast-enhanced abdominal MRI.

<b><i>Objective:</i></b> Cardiovascular risk stratification based on traditional risk factors lacks precision at the individual level. While coronary artery calcium (CAC) scoring enhances risk prediction by detecting calcified atherosclerotic plaques, it may underestimate risk in individuals with noncalcified plaques-a pattern common in younger type 1 diabetes (T1D) patients. Understanding the prevalence of noncalcified atherosclerosis in T1D is crucial for developing more effective screening strategies. Therefore, this study aimed to assess the burden of clinically significant atherosclerosis in T1D patients with CAC <100 using artificial intelligence (AI)-guided quantitative coronary computed tomographic angiography (AI-QCT). <b><i>Methods:</i></b> This study enrolled T1D patients aged ≥30 years with disease duration ≥10 years and no manifest or symptomatic atherosclerotic cardiovascular disease (ASCVD). CAC and carotid ultrasound were assessed in all participants. AI-QCT was performed in patients with CAC 0 and at least one plaque in the carotid arteries or those with CAC 1-99. <b><i>Results:</i></b> Among the 167 participants (mean age 52 ± 10 years; 44% women; T1D duration 29 ± 11 years), 93 (56%) had CAC = 0, 46 (28%) had CAC 1-99, 8 (5%) had CAC 100-299, and 20 (12%) had CAC ≥300. AI-QCT was performed in a subset of 52 patients. Only 11 (21%) had no evidence of coronary artery disease. Significant coronary stenosis was identified in 17% of patients, and 30 (73%) presented with at least one high-risk plaque. Compared with CAC-based risk categories, AI-QCT reclassified 58% of patients, and 21% compared with the STENO1 risk categories. There was only fair agreement between AI-QCT and CAC (κ = 0.25), and a slight agreement between AI-QCT and STENO1 risk categories (κ = 0.02). <b><i>Conclusion:</i></b> AI-QCT may reveal subclinical atherosclerotic burden and high-risk features that remain undetected by traditional risk models or CAC. These findings challenge the assumption that a low CAC score equates to a low cardiovascular risk in T1D.

This study aimed to explore whether an artificial intelligence iterative reconstruction (AIIR) algorithm combined with low-dose aortic computed tomography angiography (CTA) demonstrates clinical effectiveness in assessing preoperative access for transcatheter aortic valve implantation (TAVI). A total of 109 patients were prospectively recruited for aortic CTA scans and divided into two groups: group A (n = 51) with standard-dose CT examinations (SDCT) and group B (n = 58) with low-dose CT examinations (LDCT). Group B was further subdivided into groups B1 and B2. Groups A and B2 used the hybrid iterative algorithm (HIR: Karl 3D), whereas Group B1 used the AIIR algorithm. CT attenuation and noise of different vessel segments were measured, and the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated. Two radiologists, who were blinded to the study details, rated the subjective image quality on a 5-point scale. The effective radiation doses were also recorded for groups A and B. Group B1 demonstrated the highest CT attenuation, SNR, and CNR and the lowest image noise among the three groups (p < 0.05). The scores of subjective image noise, vessel and non-calcified plaque edge sharpness, and overall image quality in Group B1 were higher than those in groups A and B2 (p < 0.001). Group B2 had the highest artifacts scores compared with groups A and B1 (p < 0.05). The radiation dose in group B was reduced by 50.33% compared with that in group A (p < 0.001). The AIIR algorithm combined with low-dose CTA yielded better diagnostic images before TAVI than the Karl 3D algorithm.

BackgroundNailfold capillaroscopy enables visualisation of structural abnormalities in the microvasculature of patients with systemic sclerosis (SSc). The objective of this feasibility study was to determine whether multispectral imaging could provide functional assessment (differences in haemoglobin concentration or oxygenation) of capillaries to aid discrimination between healthy controls and patients with SSc. MSI of nailfold capillaries visualizes the smallest blood vessels and the impact of SSc on angiogenesis and their deformation, making it suitable for evaluating oxygenation-sensitive imaging techniques. Imaging of the nailfold capillaries offers tissue-specific oxygenation information, unlike pulse oximetry, which measures arterial blood oxygenation as a single-point measurement. MethodsThe CAPoxy study was a single-centre, cross-sectional, feasibility study of nailfold capillary multispectral imaging, comparing a cohort of patients with SSc to controls. A nine-band multispectral camera was used to image 22 individuals (10 patients with SSc and 12 controls). Linear mixed-effects models and summary statistics were used to compare the different regions of the nailfold (capillaries, surrounding edges, and outside area) between SSc and controls. A machine learning model was used to compare the two groups. ResultsPatients with SSc exhibited higher indicators of haemoglobin concentration in the capillary and adjacent regions compared to controls, which were significant in the regions surrounding the capillaries (p<0.001). There were also spectral differences between the SSc and controls groups that could indicate differences in oxygenation of the capillaries and surrounding tissue. Additionally, a machine learning model distinguished SSc patients from healthy controls with an accuracy of 84%, suggesting potential for multispectral imaging to classify SSc based on structural and functional microvascular changes. ConclusionsData indicates that multispectral imaging differentiates between patients with SSc from controls based on differences in vascular function. Further work to develop a targeted spectral camera would further improve the contrast between patients with SSc and controls, enabling better imaging. Key messagesMultispectral imaging holds promise for providing functional oxygenation measurement in nailfold capillaroscopy. Significant oxygenation differences between individuals with systemic sclerosis and healthy controls can be detected with multispectral imaging in the tissue surrounding capillaries.