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.

PurposeThis study evaluates the capability of diffusion-based generative models to reconstruct diagnostic-quality renal cortical images from reduced-acquisition-time pediatric 99mTc-DMSA scintigraphy. Materials and MethodsA prospective study was conducted on 99mTc-DMSA scintigraphic data from consecutive pediatric patients with suspected urinary tract infections (UTIs) acquired between November 2023 and October 2024. A diffusion model SR3 was trained to reconstruct standard-quality images from simulated reduced-count data. Performance was benchmarked against U-Net, U2-Net, Restormer, and a Poisson-based variant of SR3 (PoissonSR3). Quantitative assessment employed peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), Frechet inception distance (FID), and learned perceptual image patch similarity (LPIPS). Renal contrast and anatomic fidelity were assessed using the target-to-background ratio (TBR) and the Dice similarity coefficient respectively. Wilcoxon signed-rank tests were used for statistical analysis. ResultsThe training cohort comprised 94 participants (mean age 5.16{+/-}3.90 years; 48 male) with corresponding Poisson-downsampled images, while the test cohort included 36 patients (mean age 6.39{+/-}3.16 years; 14 male). SR3 outperformed all models, achieving the highest PSNR (30.976{+/-}2.863, P<.001), SSIM (0.760{+/-}0.064, P<.001), FID (25.687{+/-}16.223, P<.001), and LPIPS (0.055{+/-}0.022, P<.001). Further, SR3 maintained excellent renal contrast (TBR: left kidney 7.333{+/-}2.176; right kidney 7.156{+/-}1.808) and anatomical consistency (Dice coefficient: left kidney 0.749{+/-}0.200; right kidney 0.745{+/-}0.176), representing significant improvements over the fast scan (all P < .001). While Restormer, U-Net, and PoissonSR3 showed statistically significant improvements across all metrics, U2-Net exhibited limited improvement restricted to SSIM and left kidney TBR (P < .001). ConclusionSR3 enables high-quality reconstruction of 99mTc-DMSA images from 4-fold accelerated acquisitions, demonstrating potential for substantial reduction in imaging duration while preserving both diagnostic image quality and renal anatomical integrity.

PURPOSEThe VISION study1 found that Lutetium-177 (177Lu)-PSMA-617 ("Lu-177") improved overall survival in metastatic castrate resistant prostate cancer (mCRPC). We assessed whether artificial intelligence enhanced PSMA imaging in mCRPC patients starting Lu-177 could identify those with better treatment outcomes. PATIENTS AND METHODSWe conducted a single site, tertiary center, retrospective cohort study in 51 consecutive mCRPC patients treated 2022-2024 with Lu-177. These patients had received most standard treatments, with disease progression. Planned treatment was Lu-177 every 6 weeks while continuing androgen deprivation therapy. Before starting treatment, PSMA images were analyzed for SUVmax and quantified tumor volume using artificial intelligence software (aPROMISE, Exinni Inc.). RESULTSFifty-one mCRPC patients were treated with Lu-177; 33 (65%) received 4 or more treatment cycles and these 33 had Kaplan-Meier median overall survival (OS) of 19.3 months and 23 (70%) surviving at 24 month data analysis. At first cycle Lu-177, these 33 had significantly more favorable levels of serum albumin, alkaline phosphatase, calcium, glucose, prostate specific antigen (PSA), ECOG performance status, and F18 PSMA imaging SUV-maximum values - reflecting PSMA "target expression". In a "protocol-eligibility" analysis, 30 of the 51 patients (59%) were considered "protocol-eligible" and 21 (41%) "protocol-ineligible" based on initial clinical parameters, as defined in Methods. "Protocol-eligible" patients had OS of 14.6 mo and 63% survival at 24 months. AI-enhanced F18 PSMA quantified imaging found "protocol-eligible" tumor volume in mL to be only 39% of the volume in "ineligible" patients. CONCLUSIONIn this cohort of mCRPC patients receiving Lu-177, pre-treatment AI-assisted F18 PSMA imaging finding higher PSMA SUV / lower tumor volume associated with the patients ability to have four or more treatment cycles, protocol eligibility, and better overall survival. KEY POINTSO_ST_ABSQuestionC_ST_ABSIn mCRPC patients initiating Lu-177 therapy, can AI-assisted F18 PSMA imaging identify patients who have better treatment outomes? Findings33 (65%) of a 51 consecutive patient mCRPC cohort were able to receive 4 or more cycles Lu-177. These patients had significantly more favorable serum albumin, alkaline phosphatase, calcium, glucose, PSA, performance status, and higher AI-PSMA scan SUV-maximum values, with a trend toward lower PSMA tumor volumes in mL. They had Kaplan-Meier median OS of 19.3 months and 70% survived at 24 months. AI-enhanced PSMA tumor volumes (mL) in "protocol eligible" patients were significantly lower - only 40% - than tumor volumes of "protocol ineligible" patients. MeaningIn this cohort of mCRPC patients receiving Lu-177, pre-treatment AI-assisted F18 PSMA imaging finding higher PSMA SUV / lower tumor volume associated with the patients ability to have four or more treatment cycles, protocol eligibility, and better overall survival.

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.

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.

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.

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.

<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.