Biomedical Physics & Engineering Express

Background Artificial intelligence computer-aided diagnosis (AI CAD) algorithms for screening mammography have shown promise, but independent head-to-head comparisons of commercial algorithms on large, diverse cohorts remain limited. Methods 786,124 mammography screening exam performed from January 1 2022 to December 31 2023 were identified from electronic medical records. Three FDA-cleared commercial algorithms (A, B, D) and the open-source academic model Mirai (C), were applied to the four standard screening views. Breast cancer within 12 months was ascertained through cancer registry linkage. Algorithms were compared by AUROC; sensitivity, specificity, and PPV at matched AI-positive rates of 5%, 10%, 15%, 20%, 25%; and the proportion of false negatives classified as AI-positive. Results Of 707,922 examinations with complete data, 4,436 were associated with cancer. Radiologists recalled 6.8% of examinations, with a sensitivity of 67.2%. Algorithm D had the highest AUROC (0.849), followed by algorithm B (0.818), Mirai (0.817), and algorithm A (0.815). Algorithm D also had the highest sensitivity at every AI-positive rate, from 55.3% (95% CI: 54.1, 56.6) at 5% to 77.9% (95% CI: 76.9, 79.0) at 25%, with CIs that did not overlap those of the other algorithms. No algorithm matched radiologist sensitivity at AI-positive rates of 10% or less. Algorithm B flagged a proportion of radiologist false negatives similar to that of algorithm D from 5% through 20% (20.8% vs 20.5% at 5%), despite its lower standalone sensitivity. Conclusions AI CAD algorithms differed meaningfully in performance on the same large, diverse screening examination cohort, and an open-source academic model performed comparably to two of the three commercial products. Independent local validation at clinically relevant operating points should precede adoption of mammography AI, and algorithm choice and threshold should be matched to the intended workflow.

Background: Precise localisation of intracranial electrode contacts is essential for the interpretation of stereoelectroencephalography recordings and planning epilepsy surgery. In current clinical practice, this is typically a manual process, which is time-consuming and prone to variability. Existing automated solutions are often fragmented across multiple tools requiring technical expertise, limiting their adoption in routine clinical workflows. This study presents an open-source extension for 3D Slicer that provides an integrated, user-friendly standalone solution for the direct automatic detection of electrode contacts within a widely used medical imaging platform. Results: The proposed method combines anchor bolt-based initialisation, probabilistic segmentation of electrode structures, and non-linear modelling to precisely track true electrode trajectories. The approach was evaluated on a dataset comprising 78 cases from 73 patients, including 1,078 electrodes with 14,480 contacts. The method achieved high localisation accuracy, with a median (interquartile range) deviation of 0.10 (0.06, 0.15) mm. Only 7/1078 (0.65%) electrodes required manual correction; these specific cases were handled using tools provided within the proposed extension. Conclusions: The presented extension enables fast, accurate, and reproducible electrode contact localisation within a single integrated environment. By combining automation with intuitive user interaction, it significantly reduces processing time while maintaining clinical reliability. The tool's free availability as an extension in 3D Slicer lowers the barrier to adoption and supports the standardisation of workflows across clinical and research centres.

Accurate breast lesion segmentation in ultrasound is difficult because speckle noise, heterogeneous lesion appearance, and poorly defined margins can obscure clinically relevant boundaries. This work presents UGB-SegNet, an uncertainty-guided breast ultrasound segmentation network that combines four components: multi-scale convolutional block attention (CBAM) at three EfficientNet-B0 encoder stages, a learnable feature pyramid network for adaptive multi-scale fusion, an entropy-guided decoder that routes fine-grained encoder information toward uncertain regions, and a boundary-aware composite loss with deep supervision. The model was evaluated on the public BUSI dataset (647 annotated lesion images) using a stratified 454/64/129 train/validation/test split. On the held-out test set, UGB-SegNet achieved 81.30% Dice, 68.49% IoU, 81.71% sensitivity, 98.14% specificity, and 15.63-pixel HD95. Under identical training conditions it outperformed five directly trained baselines: U-Net, Attention U-Net, SegNet, DeepLabV3+, and TransUNet. A five-fold resampling experiment over the development set with the held-out test set fixed yielded a mean Dice of 76.36% +/- 1.66% (95% CI 74.90-77.81). Reported ablations attribute gains of +2.06 Dice points to multi-scale CBAM, +1.52 to learnable FPN fusion, +1.22 to the uncertainty-guided decoder, and +1.24 to boundary-aware training. UGB-SegNet contains 14.00M parameters and runs at 62.4 FPS at 224 x 224 resolution on the tested NVIDIA GPU, providing a compact accuracy-efficiency trade-off for future prospective clinical validation.

Background: MRI is widely used in radiotherapy planning due to its high soft-tissue contrast, but geometric distortions can compromise target localization accuracy. Objective: This study aimed to develop an accessible method for assessing geometric distortion in MRI using two phantoms - a commercial anthropomorphic phantom and a custom-made phantom fabricated from ABS plastic. Approach: CT imaging was used as the reference standard. Distortion was assessed through linear measurements of periodic structures in a DICOM viewer, followed by statistical analysis. Significance: The study evaluates the clinical impact of distortion on radiotherapy planning and proposes a cost-effective solution for routine quality assurance in resource-limited settings.

Background. Roughly, half of new type 1 diabetes (T1D) diagnoses occur in individuals under 18 years old and represent a more aggressive destruction of beta cell mass (BCM). [11C]-(+)-PHNO positron emission tomography (PET) imaging is used to assess BCM, but current pancreas PET imaging protocols are limited to adults. Previously published full count data from six healthy controls and five T1Ds (6M/5F; 22 to 53 years old) were used for retrospective analysis. Dynamic [11C]-(+)-PHNO PET/CT scans were acquired and reconstructed using full-count list-mode data. For the current comparison to full count data, 50%, 25% and 10% down-sampled count data were re-reconstructed. Pancreas and spleen (reference region) time-activity-curves (TACs) were assessed, and volume of distribution (VT, mL/cm3) was estimated using the reversible 1-tissue compartment model (1TC) with tmax of 30 min for all count levels. Pancreas and Spleen VT estimates (1TC; tmax= 30 min) were used to calculate non-displaceable binding potential (BPND) and were then correlated to semi-quantitative methods of standardized uptake value ratio (SUVR-1) (20-30 min; ref: spleen) to examine simplified methods using simulated low dose protocols. Finally, we performed dosimetry in adult, adolescent and pediatric phantoms to assess radiation dose for simulated low-dose protocols. Results. Qualitatively, increasing noise can be visualized at successive reduced-count levels images, compared to full-count images. Despite progressively increasing noise in reduced-count images, TACs at each reduced-count level remained similar to full-count TACs in both HC and individuals with T1D. Quantitatively, 1TC VT estimates were similar for all reduced count levels and range of tmax values, compared to full-count (all R2[≥]0.99). Pancreas SUVR-1 (20-30 min) and pancreas BPND (tmax = 30; ref: spleen) were highly correlated for all count levels (all R2[≥]0.80). All age groups were under both the yearly occupational and research scan radiation dose limits when examining mean effective dose equivalent with reduced (1/10th) injected dose protocols. Conclusion. Low-count reconstructed data and simplified reference region approaches provide accurate quantification compared to full-count reconstructions. These results provide evidence that it is possible to perform accurate quantification using simulated low dose protocols to quantify BCM for use in individuals with T1D under 18 years old.

Answer accuracy alone cannot determine whether a Vision-Language Model (VLM) relies on clinically relevant mammographic evidence or recognizes when that evidence is unavailable. We introduce an evidence-grounded selective evaluation benchmark that evaluates Pathology classification and Abnormality identification together with label-aware lesion localization. Each original image--task instance is paired with a lesion-removal counterfactual and, when feasible, a size-matched non-lesion random-removal control. Lesion evidence is removed by local tissue reconstruction, while the random-removal view provides a task-irrelevant regional perturbation baseline of comparable spatial extent. We evaluate 16 general-purpose and medically specialized VLMs, including open-source and proprietary models, under a common protocol with the same prompt, image conditions, label spaces, and structured-output requirements. The results show substantial gaps between classification performance and evidence-grounded reliability. InternVL3.5-8B achieved the highest Pathology Macro-F1 at 53.75\%, Huatuo Vision-34B achieved the highest Abnormality Exact Match at 52.44\%, and GPT-5.6 Sol achieved the highest Grounded Answer Success at 21.99\%. Lingshu-7B achieved the highest Counterfactual Specificity at 35.19\%, whereas GPT-5.6 Sol achieved the highest Joint Reliability at 6.47\%. These findings suggest that answer accuracy and abstention behavior can substantially overstate the reliability of current VLMs when predictions are not verified against the visual evidence on which they should depend.

Fibroepithelial breast lesions include fibroadenomas (FAs) and phyllodes tumors (PTs), which are difficult to distinguish preoperatively due to their overlapping clinical and ultrasound features. While FAs are typically managed with observation, PTs require complete surgical excision with negative margins due to their risk of local recurrence and malignant progression. Because core needle biopsy can be inconclusive in about 15% of patients, some patients undergo unnecessary surgical procedures. Breast ultrasound (BUS) is a widely available, noninvasive imaging modality, but visual interpretation alone is limited by the overlap in lesion appearance. Artificial intelligence (AI)-based BUS offers the potential to improve the preoperative differentiation of PTs from FAs, reducing unnecessary operations while ensuring appropriate treatment for patients with PTs. Most deep learning approaches operate at the image level, whereas clinical diagnosis is made at the patient level by integrating multiple ultrasound views with clinical context. We propose a patient-level multimodal multiple instance learning (MIL) framework for fibroepithelial tumor diagnosis in breast ultrasound. Each patient is represented as a bag of BUS images encoded by an ultrasound foundation model (USFM) and aggregated using attention-based MIL to produce an image-derived patient score. Clinical variables, including age, lesion size, and echogenicity, are modeled using a gradient-boosted clinical learner. The image and clinical scores are then combined using logistic score fusion. In patient-level 5-fold cross-validation, logistic score fusion achieved 0.780 AUC-ROC for classification, improving over clinical feature-only gradient boosting (GB) by +0.033 (95\% CI [+0.013, +0.053], p = 0.001), image-only MIL by +0.081 (95\% CI [+0.042, +0.119], p < 0.001), and MLP-based end-to-end multimodal fusion by +0.035 (95\% CI [+0.003, +0.067], p = 0.030). These results suggest that patient-level score fusion can combine complementary ultrasound and clinical evidence for PTs vs FAs.

Background: Brachytherapy delivers localized radiation from sources placed within or adjacent to tissue at risk. In brain tumor surgery, intracavitary implantation can begin at resection and concentrate dose along the cavity wall, where many recurrences arise. Current preoperative GammaTile tools estimate tile requirements but not patient-specific placement, resulting dose, or adjacent anatomy. We developed an integrated software framework for patient-specific virtual GammaTile planning. Methods: The framework incorporates AI-assisted tumor and cavity segmentation with user approval, converts accepted masks into patient-derived surfaces, supports virtual tile placement, calculates lifetime Cs-131 dose using TG-43, maps dose distributions to HCP-MMP cortical parcels and normative HCP-1065 white-matter bundles, and supports AI-assisted synthesis of the resulting complex anatomical and connectomic output for clinician review. We retrospectively applied it to three patients with glioblastoma. Tile requirements were compared with the GammaTile Cavity Surface Area Calculator. For Patient 1, calculated 60- and 80-Gy volumes were compared with the clinical plan. Results: Preoperative layouts required 6.5, 7, and 4 tile equivalents for Patients 1-3; calculator estimates were higher by 1.5, 4, and 1 tiles. Postoperative differences narrowed to 1, 1, and 0 tiles. The plans generated patient-specific lifetime dose distributions at 60, 80, and [&ge;]120 Gy. In Patient 1, calculated and clinical 60- and 80-Gy volumes were similar despite incomplete spatial overlap. Atlas mapping revealed distinct cortical and white-matter exposure patterns across cases beyond lobar description. Conclusions: Patient-specific virtual GammaTile planning can connect anticipated tile placement with calculated dose and adjacent neuroanatomy. These cases demonstrate feasibility and establish a framework for prospective validation and future comparison of candidate implant arrangements.

Background. Accurate delineation of the gross tumour volume (GTV) - primary tumour (GTVp) and nodal disease (GTVn) - on FDG-PET/CT is a critical step of head and neck radiotherapy planning. Comparisons between lightweight custom networks and the auto-configured nnU-Net v2 are usually reported as end-to-end pipelines, conflating the contribution of the network with that of the inference-time post-processing applied on top of it. We separated the two. Methods. MiniUNet3D (custom 3D U-Net, 18.3 M parameters) and nnU-Net v2 (3d_fullres, 88.2 M parameters) were trained on the same 578 FDG-PET/CT cases (85/15 author-defined split of the HECKTOR 2025 Task 1 set, 8 centres) and evaluated on the same internal cohort. Three arms were compared pairwise: MiniUNet3D raw output at a fixed 0.5 threshold, MiniUNet3D with a locked adaptive post-processing pipeline, and nnU-Net v2. Comparisons used paired Wilcoxon tests with bootstrap confidence intervals, Bonferroni and Benjamini-Hochberg correction, and Cohen's d; catastrophic failure (Dice < 0.01) was compared with an exact McNemar test. Cases with an empty reference for a given target were excluded from that target's analysis (n = 98 GTVp, n = 93 GTVn). Results. With post-processing matched off, nnU-Net v2 was superior: median GTVp Dice 0.799 versus 0.592 (mean difference -0.244, 95 % CI -0.300 to -0.191; d = -0.88) and GTVn 0.774 versus 0.598 (d = -0.82). Post-processing raised MiniUNet3D to 0.800 (GTVp) and 0.738 (GTVn), recovering 79 % of that difference. Post-processed, MiniUNet3D matched nnU-Net v2 on GTVp Dice (p = 0.113) but remained inferior on nodal disease after Bonferroni correction (Dice p = 0.041; surface Dice p = 0.049). Catastrophic GTVp failures were 25/98 raw, 8/98 post-processed and 1/98 for nnU-Net v2 (McNemar p = 0.016). Inference took 34 s versus 78 s per case on the same GPU. Conclusions. Post-processing recovered most, but not all, of the difference between the two models, and it did not confer robustness: an eight-fold higher rate of empty contours on small primaries persisted, which is the more consequential difference for planning safety. Pipeline comparisons reported without a post-processing ablation risk attributing to a network what post-processing supplied.

Head and neck radiation oncology, with its complex locality and epidemiology, requires various imaging techniques to achieve sufficient visualization. Through collaboration with medical physicists, radiation therapists, radiation oncologists, and neuroradiologists, our group clinically implemented a standard set of magnetic resonance imaging (MRI) sequences on an MR-simulation scanner for three anatomical regions: skull base, oral cavity, and larynx/pharynx. A total of 41 patients with head and neck cancer and 3 volunteers were scanned on either a 1.5T or 3T MR-simulation scanner. Sequences described include Dixon-based approaches, SPAIR-based approaches, short T2-based approaches, and quantitative imaging methods. We describe and outline the steps to optimize these notable MRI sequences to aid in successful implementation in a radiation oncology clinic.

Background and Objective: Micro-ultrasound (MicroUS) is an emerging high-resolution imaging modality for prostate cancer diagnosis and real-time biopsy guidance, but its automated analysis is limited by labor-intensive expert annotation. We aimed to develop and evaluate a foundation model for data-efficient MicroUS image analysis. Methods: We developed muFM, a MicroUS-specific foundation model pretrained using DINOv2-style self-supervised learning on 1,736,391 unlabeled images from 780 patients. The model was fine-tuned and evaluated in three downstream cohorts for prostate capsule segmentation (public, 75 patients), urethra segmentation (institutional, 105 patients), and prostate cancer lesion localization (institutional, 190 patients). Under limited annotation, muFM was compared with randomly initialized models and foundation models pretrained on natural, medical, and ultrasound images. Evaluation used the Dice scores and 95th-percentile Hausdorff distance (HD95) for segmentation and free-response receiver operating characteristic analysis for lesion localization. Key Findings and Limitations: muFM improved performance across all three tasks, with the largest gains under limited annotation. With 10% of training scans, muFM achieved Dice scores of 93.8% for prostate capsule segmentation and 58.4% for urethra segmentation, versus 87.9% and 47.0% for the strongest comparators. Corresponding HD95 values were 2.0 and 4.3 mm. For lesion localization, muFM outperformed other models with approximately 0.75 sensitivity at two false-positive detections per 3D volume. Limitations include retrospective single-institution pretraining and lack of explicit 3D modeling. Conclusions and Clinical Implications: MicroUS-specific self-supervised pretraining improved data-efficient image analysis across prostate and urethra segmentation, and lesion localization. This approach may reduce annotation requirements for developing automated tools in MicroUS image analysis.

Background. Deep denoisers restore low-dose chest radiographs but emit a single point estimate with no indication of where the output is reliable, so a confident-looking reconstruction can be locally wrong. In paediatric radiography, where the radiation-dose imperative is sharpest, we ask whether a per-pixel uncertainty map can be attached at no meaningful cost to reconstruction quality. Methods. We develop PP-VAE-Hformer, a hybrid convolution-transformer denoiser with a variational-autoencoder bottleneck (epistemic uncertainty map) and a heteroscedastic dual head (aleatoric map), and run a 19-arm ablation isolating five composite-loss terms on Poisson-Gaussian-degraded paediatric chest radiographs (Kermany collection; 624-image held-out test set), against eight discriminative baselines retrained on identical data and noise. Calibration is assessed by reliability diagrams and post-hoc-scaling; comparisons use Bonferroni-corrected Welch tests with Cohens d as the primary discriminant. Results. A heteroscedastic negative-log-likelihood (NLL) objective buys a calibrated aleatoric map for 1.2 dB PSNR, which two-stage fine-tuning recovers to a statistically indistinguishable 0.010 dB. For the variational variant the Kullback-Leibler (KL) annealing schedule, not the bottleneck itself, is decisive: cyclic annealing closes a 1.10 dB gap over posterior-collapsing linear warmup. Predicted uncertainty tracks realised error (Pearson r > 0.8) with an optimal recalibration scale within 1% of unity. A matched-severity cross-degradation test shows this calibration is model-circular, however: at equal severity the aleatoric map becomes modestly (8-16%, a lower bound) over-confident once the noise model changes, and the Monte-Carlo epistemic map is a weaker error predictor that does not compensate. We additionally document a previously unreported destructive interaction between structural-similarity and Sobel-edge supervision at literature-default weights. Conclusions. A calibrated per-pixel uncertainty map can be attached to a paediatric low-dose CXR denoiser at effectively no reconstruction cost. Its near-perfect calibration is tied to the trained (inverse-crime) noise model, and the Monte-Carlo epistemic map does not reliably substitute where the aleatoric map fails, so an architecture-independent uncertainty estimate (e.g. deep ensembling), a blinded reader study, and real low-dose data are the immediate next steps. Keywords. image denoising; uncertainty quantification; calibration; variational autoen-coder; paediatric chest radiography; low-dose imaging

Purpose: Attenuation correction (AC) of PET images is essential for accurate quantification. Brain PET studies comprising simultaneous EEG (PETEEG) may suffer from metal artifacts in CT images (CTEEG), or improper correction when electrodes are not present in the CT (CT0). As these influences are not well-characterized, we aim to compare metal artifact reduction (MAR) techniques for CTEEG images, and evaluate differences between attenuated-corrected PETEEG using CT0 and CTEEG with MAR, synthetically placed electrodes (CTEEG-synth) and extended Hounsfield unit (HU) range. Methods: 19 healthy participants underwent two total-body PET/CT scans with [18F]FDG, with and without 32 EEG scalp electrodes, respectively. We evaluated five MARs to reduce streaks caused by the EEG electrodes in the CTEEG. Finally, CT0, CTEEG with (CTEEG-iMAR-Ext) and without extended HU range (CTEEG-iMAR) and CTEEG-synth were used to perform attenuation correction of PETEEG. We compared our results to PET0/CT0 scan using relative differences. Results: CTEEG and CTEEG-iMAR showed the smallest differences to CT0. PETEEG/CTEEG-iMAR-Ext exhibited the lowest differences to PET0/CT0 (average bias across all regions of -0.46%), followed by similar performance of PETEEG/CTEEG-iMAR (-0.73%) and PETEEG/CTEEG (-0.76%). Conversely, PETEEG/CT0 demonstrated the largest average differences (-1.81%), with values reaching -2.71% in the parietal lobe. These differences were consistent across subjects, yielding significant effects in most of the brain (pFWE < 0.05). CTEEG-synth performed not as good as CTEEG (-1.21%). Conclusions: CTEEG with extended HU range is most suitable for attenuation correction of PETEEG images, with MAR correction offering little additional improvement.

Augmented reality glasses typically have poor optical efficiency (<1%), which is detrimental for high power applications, such as prosthetic vision. Maxwellian view optics are a candidate for increasing efficiency, but suffer from a very limited exit pupil and thus field of view. Many approaches to extend the eyebox inherently decrease the optical power. We present a design that uses a high-density VCSEL array and eye tracking to create an extended eyebox without sacrificing the advantages of a Maxwellian view display. Our system achieves a 20 degree field of view, a resolution 48.8 LP/mm (10 m), and an end-to-end optical efficiency of 83%

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Objectives: To develop and validate pixel data analysis (PDA) for reconstructing pixel-level shear-wave velocity (SWV) from saved Holder-Optimized Elastography (HOE) elastograms and to determine whether whole-field subcutaneous heterogeneity provides a quantitative signature of lymphatic obstruction compared with visual high-velocity-area (HVA) counting and sparse manual region-of-interest (ROI) sampling. Materials and Methods: In this cross-sectional study, 110 women after breast cancer surgery underwent lymphoscintigraphy and HOE, a pressure-minimized acquisition framework that preserves conventionally captured SWV information while additionally revealing spatial abnormalities attenuated by handheld acquisition. Reconstruction validation used 200 paired ROIs from 49 PACS-retrieved images spanning nearly the full displayed SWV range; reproducibility was assessed across 96 matched anatomical sites from 6 participants undergoing independently reacquired HOE examinations. PDA, visual HVA counting, and manual ROI sampling were evaluated on the same HOE-acquired elastograms in task-specific common complete-case cohorts. Results: PDA reconstructed machine-reported SWV with near-zero error (mean difference, +0.0009 m/s; mean absolute error, 0.0075 m/s; 95% limits of agreement, -0.0202 to +0.0221 m/s). For subcutaneous variance, interobserver/intraobserver ICCs were 0.718/0.799 for PDA and 0.161/0.560 for manual ROI sampling. In the primary no-versus-partial comparison (n = 93), forearm-pooled PDA subcutaneous variance ratio yielded the highest observed AUC (0.859; 95% CI, 0.772 - 0.947), compared with 0.820 for visual HVA counting and 0.769 for manual ROI sampling. PDA's subcutaneous variance ratio remained a leading feature across all three comparisons (AUC, 0.859 - 0.878), including partial-versus-total grading, where all three frameworks showed high discrimination. Conclusions: PDA accurately reconstructs pixel-level SWV from saved HOE elastograms and enables reproducible whole-field quantification of superficial SWE heterogeneity. Subcutaneous variance ratio was the leading quantitative PDA feature across lymphatic-obstruction comparisons and was particularly informative in partial obstruction. The same approach also enables retrospective quantitative reanalysis of compatible archived elastograms.

Objective To evaluate the effect of vendor-integrated AI-assisted abdominal ultrasound software on operational efficiency and sonographer workload compared with manual scanning. Methods In this prospective randomised crossover study (January to February 2026), 32 healthy adults each underwent two upper abdominal examinations, one manual and one using vendor-integrated AI software (AI Abdomen Release 3.5; ACUSON Sequoia), in randomised order by two experienced sonographers, each participant scanned once by each sonographer. Scan time, hand-console interaction (keystrokes, hand travel, hover, jerk) from a custom depth-camera hand-tracking system, and operator modifications to AI outputs were recorded. Workload was assessed after each scan with the weighted NASA Task Load Index (NASA-TLX). Analysis used linear mixed-effects models. Results AI-assisted scanning reduced scan time (52.4 s, approximately 9%; 95% CI 23.7 to 81.2; P = 0.001), keystrokes (55, approximately 28%; P < 0.001) and hand travel (4.57 m, approximately 39%; P < 0.001), although the time saving was concentrated in one sonographer. Weighted NASA-TLX did not differ between conditions (-3.9 points; 95% CI - 9.3 to 1.5; P = 0.17), but subscale analyses showed reductions in mental demand (- 6.3; P = 0.03) and effort (- 7.0; P = 0.04), with no compensating increases. Sonographers modified 48 of 184 AI-generated values. Conclusion AI assistance improved operational efficiency and reduced self-reported mental demand and effort, with no compensating increase on other subscales. Gains arose under a controlled, abbreviated protocol in healthy volunteers and varied between operators, and are better read as a reshaping of operator work than its removal.

A new electrical stimulation focused computational model of the visual cortex had been created to aid in the development of cortical visual prosthesis. The model consists of 10,666 biophysical neurons representing 0.13mm3 of a layer 2/3 of the primary visual cortex and was calibrated to match the baseline activity of rat brain recordings. A novel model of electrical stimulation was developed to allow for selective activation of specific neuron types, and matched the single cell stimulation response generated by known stimulation models. The electrode was tuned to match recorded population level change in activity across distances and currents recorded in the rats brain. The model is now ready to explore electrical stimulation effects on the visual cortex for examination of neuron specific stimulation to assist in the development of cortical visual prosthesis.

The sonography profession faces a persistent occupational health crisis, with nearly 90% of practitioners reporting work-related musculoskeletal disorders (WRMSDs) despite two decades of ergonomic education. This study performs a definitive bibliometric analysis to map the evolution of ergonomic technology and interventions for WRMSDs from 2000 to 2025. Addressing this unabated prevalence of injury is paramount to ensuring the long-term sustainability and resilience of the imaging workforce amid rising clinical demands. Adhering to PRISMA guidelines, the methodology utilized the Scopus database to extract a curated dataset of 93 peer-reviewed publications. Quantitative and network analyses were performed using VOSviewer and the Bibliometrix R-package to evaluate publication growth, leading contributors, and thematic clusters. The findings reveal a conceptual shift from foundational hazard identification toward high-order technological solutions, such as workstation automation and specialised transducer handles. Notably, the results indicate that traditional education-only models have reached a maximum threshold, as environmental constraints and high patient volumes frequently override individual behavioural changes. By providing the first integrated evaluation of the transition from awareness to technology-driven intervention, this study fills a critical gap in the literature regarding the structural role of technology as a prevention support tool. In practice, these findings provide a roadmap for hospital administrators and manufacturers to prioritise proactive environmental transformation over individual posture correction to safeguard practitioners' health. Keywords: Sonographer Ergonomics; musculoskeletal disorders (WRMSDs); Bibliometric Mapping; Technological Intervention; Workforce Sustainability.

Electrocutaneous stimulation provides a means to warn workers of potential hazards. Our previous parameter studies were conducted at rest or under controlled external influences such as vibration, temperature, and humidity, whereas actual work tasks have not yet been addressed. As a step towards greater practical applicability, we conducted two studies in which a circumferential electrocutaneous warning signal was applied to the upper right arm while participants performed a reading task, screw-driving, and polishing. In study 1 (n = 32), the pulse interval was adjusted individually to evoke a vibrating sensation, and the intolerance threshold was determined. In study 2 (n = 29), a fixed pulse interval of 36 ms was applied to all participants, the warning threshold was determined in addition to the intolerance threshold, and muscle twitches were documented both by participant report and by an independent observer. Study 1 demonstrated that electrocutaneous warning during work tasks is feasible, with a median intolerance threshold higher during polishing (16.5 mA) than during reading or screw-driving (14 mA each). In study 2, the fixed parameter setting elicited the intended pulsating or vibrating sensation at rest in 89 % of the participants in both presentations. The median warning threshold was 8 mA and did not differ between the three work tasks, whereas the intolerance threshold was lower during reading (18 mA) than during screw-driving and polishing (20 mA each). Muscle twitches were largely absent at the warning threshold and became frequent towards the intolerance threshold, with a ventral dominance of their location in both studies. An individual parametrization is therefore not required for the majority of users when a binary warning is to be conveyed, and operating the system slightly above the warning threshold provides a usable amplitude range while minimizing muscle twitches. Future work will focus on electrode optimization and on the objective measurement of motor responses.