Bioengineering

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning

Introduction: Critical-sized bone defects cannot heal spontaneously, requiring additional, often burdensome, treatment. Thus, various synthetic substitute materials have been investigated regarding their treatment capacity. Poly-L-lactic acid (PLLA) and polyglycolic acid (PGA) have emerged as promising biodegradable scaffold materials. The addition of inorganic materials such as calcium carbonate (CC) has also been shown to be advantageous. This study investigates the effect on bone regeneration of PLLA-PGA-CC scaffolds in critical-sized bone defects over a two-year observation period using sheep as an animal model. Methods: Critical-sized mandible angle defects were created in twelve female merino sheep. Mandibular defects were reconstructed with PLLA-PGA-CC scaffolds in four sheep, while the remaining eight served as negative control (defects left empty). The scaffolds were manufactured using computer-aided design and manufacturing, incorporating an interconnected porous structure and fixated with polyether ether ketone cages. Bone regeneration was evaluated using computed tomography (CT) imaging at 3, 12, and 24 months postoperatively. Bone volume was assessed quantitatively. Additionally, a histological analysis was performed. Results: Surgical procedures were successful and without major complications. CT assessment showed more bone regeneration in the scaffold group (mean volume: 7,472 mm3) than in the control group (4,168 mm3, p = 0.1) at 24 months postoperatively. Resorption of the scaffolds and formation of compact lamellar bone tissue were confirmed by histological analysis. However, the osteoconductive properties of the scaffolds were limited, with only minimal ingrowth of bone tissue into the porous structure. In both groups, fibrous tissue infiltration and the formation of cyst-like cavities in the defect region were observed. Conclusion: PLLA-PGA-CC scaffolds were found to be biocompatible and enhanced bone regeneration compared to the control group. Due to fibrous tissue infiltration and the lack of osteoconductivity, the suitability of the material for critical-sized bone defect reconstruction is limited.

Purpose: To investigate how artificial intelligence (AI) systems detect referrable diabetic retinopathy (DR) from retinal photographs by analysing heatmap patterns and determining their overlap with DR features. Methods: Fifty-four AI systems were developed using 27 backbone architectures, with each implemented as both binary-referable and multi-class grading models based on the International Clinical Diabetic Retinopathy (ICDR) grading scale. Models were trained on images from DDR, BRSET and Kaggle datasets. After training, each model analysed 749 images with DR feature annotations, with Grad-CAM heatmaps generated and compared to pixel-level annotations of microaneurysms, haemorrhages, exudates, cotton wool spots, venous beading, intraretinal microvascular abnormalities and neovascularisation. Results: All models achieved acceptable predictive performance (AUROC >0.8 for most architectures). Heatmap analysis revealed consistent attention to the macular region with relative neglect of the optic disc. Exudates and cotton wool spots were highlighted most frequently by the heatmaps, with venous beading and neovascularisation at the disc showing poor overall coverage for binary referable classifiers. Models grading per the ICDR scale demonstrated high coverage for all features. Substantial variability was observed between architectures, suggesting different feature detection capabilities. Interestingly, the heatmap analysis indicated that the models were using different logic to the ICDR grading scale definitions. Conclusion: AI models do not uniformly rely on all DR features when detecting referable DR, limiting their predictive performance in unusual presentations. Heatmap aggregation analysis provides a scalable method for analysing model behaviour, allowing strengths and weaknesses to be identified. These findings may help improve clinician's trust and acceptance of AI.

Accurate evaluation of extracellular matrix (ECM) mineralization at the nano-scale is essential for establishing relevant in vitro bone models. This is particularly important with the development and increased application of three-dimensional (3D) cell models for biological research. Transmission electron microscopy (TEM) allows to perform ultra-structural analysis of cells and ECM organization, but its application in in vitro bone models remains limited, due to the potential alteration or loss of the mineral phase during sample preparation. In this study, we compared two TEM sample preparation methods - the conventional chemical fixation and the anhydrous methods - to evaluate their ability to preserve the mineralized ECM in MC3T3-E1 cells cultured as monolayers and as alginate-encapsulated bone spheroids. Chemical fixation preserved cellular ultra-structure and collagen organization, allowing for detailed assessment of cells and ECM organization. Although mineral deposits were detected and their needle-like morphology assessed, characterization of more immature deposits was partially limited by the effects of uranyl acetate and the overall sample preparation process, which could lead to alteration or loss of less stable mineral phases. The anhydrous preparation method resulted in limited preservation of cellular and ECM morphology and did not allow reliable identification of mineral deposits. When applied to spheroids, the chemical fixation method preserved the 3D architecture, collagen-rich ECM and inner mineral deposits, confirming spheroids as a relevant model for bone studies. Overall, these results highlight the need for optimized sample preparation strategies that preserve both ultra-structure and mineral components for accurate nano-scale characterization of bone mineralization.

AO_SCPLOWBSTRACTC_SCPLOWEarly tumor detection is critical for improving patient survival and recovery. Clinically, tissue palpation is routinely used to identify regions of abnormal stiffness, a hallmark of many pathological conditions. However, palpation is restricted to anatomically accessible sites and remains highly operator dependent. Here, we introduce a method for real-time quantitative stiffness mapping using an unmodified commercial endoscope, with the goal of enhancing diagnostic capabilities and restoring mechanical feedback during endoscopic procedures. Our approach combines shear wave elastography with speckle imaging and an innovative synchronization strategy that enables the measurement of shear wave propagation using an unmodified commercial endoscope. The resulting wave fields are analyzed with the noise-correlation-inspired (NCI) method[1], providing pixel-wise estimates of shear wave velocity and, consequently, quantitative maps of local tissue stiffness. The method demonstrated robust performance in both benchtop and endoscopic configurations. Validation was achieved on polymer phantoms as well as on ex vivo and in vivo biological tissues, highlighting its potential for minimally invasive biomechanical imaging and real-time tissue characterization.

PurposeFunctional tricuspid regurgitation (FTR) is driven by right ventricular (RV) remodeling, annular dilation, and papillary muscle dislocation. Free wall approximation (FWA) has been proposed to treat FTR by addressing RV dilation, but its effects on tricuspid valve (TV) biomechanics remain unclear. We present a real-time 3D echocardiographic (rt3DE)-based finite element framework to quantify TV biomechanics under FTR, and preliminarily apply it to assess FWA effects. MethodsSubject-specific models were developed from rt3DE data of three dilated porcine hearts in an ex-vivo mock-loop. TV geometries at end-diastole and peak systole (PS) were complemented by parametric chordae tendineae and hyperelastic tissue properties. TV closure was simulated under a standard pressure load and image-based annular motion. After tuning chordae length to replicate the PS ground truth in FTR, FWA was simulated as 30% and 60% approximations along three anatomical directions (anterior-posterior, A-P; anterior-septal, A-S; anterior-septal wall, A-SW). ResultsIn FTR simulations, median geometric errors ranged from 1.16 to 1.26 mm; median stress ranged from 56.4 to 74.7 kPa. FWA simulations predicted regurgitant orifice area (ROA) reductions by 53-99%, albeit overestimating the residual ROA vs. in vitro ground truth when starting from particularly extreme FTR conditions; concomitantly, a median stress reduction by 8-43% vs. FTR conditions was predicted. ConclusionPreliminary data suggest that our rt3DE-based framework can reliably quantify FTR-related TV biomechanics and that post-FWA biomechanics depends on initial FTR conditions. A larger cohort is required to verify the method and obtain statistically significant results.

Purpose: To develop and validate a temporal deep learning framework for predicting geographic atrophy (GA) progression across multi-year horizons using longitudinal optical coherence tomography (OCT) sequences. Design: Retrospective longitudinal cohort study. Subjects, Participants, and/or Controls: A total of 91 patients with dry age-related macular degeneration (AMD) were identified from Wake Forest University School of Medicine (2013-2023), yielding 455 OCT volumes. Two prediction cohorts were defined: 32 patients with no GA (NGA) at baseline who subsequently developed GA, and 35 patients whose earliest GA manifestation was non-central GA (NCGA). Non-progressing patients served as negative controls. Methods: OCT B-scan volumes were encoded into visit-level feature representations using three pretrained architectures (ResNet-18, ResNet-50, ViT-B/16). Chronologically ordered visit embeddings, optionally augmented with inter-visit time intervals ({Delta}t), were processed through recurrent neural networks (RNN), long short-term memory networks (LSTM), and Transformer encoders to model longitudinal disease trajectories. Models were trained and evaluated independently for prediction horizons of 2, 3, 4, 5, and 6 years using patient-level stratified splits (80/20). Performance was assessed across five random seeds. Main Outcome Measures: Area under the receiver operating characteristic curve (ROC-AUC), F1-score, and accuracy for predicting two clinically critical transitions: NGA to GA onset and NCGA to central GA (CGA) involvement. Results: For NGA to GA prediction, models achieved ROC-AUC of 0.84-0.94 at 2-4 years and 1.00 at 5-6 years. For NCGA to CGA prediction, Transformer-based models achieved peak AUC of 0.95 at 4 years and 0.96 at 5 years. Longer input sequences (8 visits vs. 4 visits) consistently improved NCGA to CGA performance at extended horizons. Temporal interval encoding improved stability in several LSTM configurations.

The human spine is a complex, coordinated biomechanical system. Physiologically, its tissues are also highly interdependent in terms of function and viability. The interaction between mechanical stress and biological/biochemical activity over time constitutes a key driver of spinal degeneration. Research to date providing mechanistic insights into this process has focused on individual components (vertebra and disc tissue analogues), in isolation or as basic functional units. However, many observations from individual units will not translate to whole spine behaviour. The intricate complexity of the spine requires novel experimental models (synthetic and biotic), which must consider the spine at an organ level and adopt an integrative approach that can capture the dynamics which govern its function. Here, we report the development of a biomimetic spinal model prototype, amenable to cellular integration, which is miniaturised to the in vitro scale to provide a controlled environment and testbed for axial biological mechanics. The research presented here encompasses more than a decade of systematic investigations during which the gradual emergence of key manufacturing innovations progressively enabled addressing an exceptionally complex bioengineering challenge - organotypic spine engineering. The model comprises the full anatomical range of spinal vertebrae/bones (C1 to Sacrum & Coccyx), reproduced using bioceramic materials, assembled in sequence into a relevant columnar architecture and mechanically connected end-to-end by biochemically active interfaces. A range of assessments examining anatomical design, material behaviour and manufacturing processes is presented. The work explores concepts such as longitudinal mechanobiology and multi-segment coupling as well as manufacturing strategies using autonomous materials and instrumentation. This prototype introduces for the first time columnar level behaviour and the ability to study time dependent adaptations. This model is important because it can support tissue maturation, evolving mechanical properties and adaptive behaviour and it represents an intermediate step between isolated skeletal tissue models and future organ-level spinal constructs.

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X-ray imaging, which provides limited depth perception and exposes patients and clinicians to ionizing radiation. Here we present an augmented reality (AR) system that fuses intravascular ultrasound (IVUS) and electromagnetic (EM) position tracking with preoperative computed tomography (CT) to produce an anatomically accurate, deformation-corrected navigational reference. A robotic device performs ECG-gated pullback of the IVUS probe, capturing 4D aortic motion across the cardiac cycle. We introduce a deep learning architecture for extracting vascular lumen boundaries and side-branch orifices from artifact-prone IVUS streams, and a semantically driven non-rigid CT-IVUS fusion pipeline robust to false positive landmarks. We evaluate the platform with trained surgeons in benchtop phantom studies and in-vivo ovine models, and demonstrate its application to fenestrated endovascular aneurysm repair (FEVAR). Compared to fluoroscopy alone, AR guidance significantly reduces cannulation time, radiation exposure, and cognitive workload, while improving procedural efficiency and safety. Our IVUS-EM and CT aortic datasets are released open source.

Understanding the neuromuscular properties that allow dexterous manipulation of objects remains a major challenge in neurorehabilitation, largely due to the difficulty of characterizing intrinsic hand muscle activity. These muscles are small, densely packed, and anatomically complex, making selective recordings with intramuscular electromyography (EMG) technically demanding and impractical for comprehensive studies. In this work, we present a custom, high-density (HD) surface EMG grid designed to non-invasively capture activity from intrinsic hand muscles from both dorsal and palmar surfaces. We evaluated the quality and spatial selectivity of the recordings by directly comparing them with intramuscular EMG signals obtained from the dorsal and palmar interossei. Surface EMG signals corresponded closely to the intramuscular recordings, with high correlation values for all subjects and tasks. Double differential spatial filtering significantly improved selectivity, although some residual volume conduction remained. The dorsal grid primarily captured dorsal interossei activity, while the palmar grid was more sensitive to lumbrical activation. The palmar interossei recordings were spatially more varied, with the second palmar interosseous predominantly detected on the dorsal grid and the third and fourth on the palmar grid. Together, these results demonstrate that non-invasive HD surface EMG will allow more complete measurement of intrinsic muscle activity, to provide a better understanding of the complex relation between the intrinsic and extrinsic hand muscles during dexterous movements. This basic information will allow refinement of biomechanical hand models and prosthetic devices, and the development of biomimetic brain computer interfaces aimed at restoring natural hand function after neurological injury.

Purpose: To determine whether disease-aware adversarial perturbations can reduce demographic recoverability encoded in color fundus photographs (CFPs) while preserving glaucoma-related diagnostic features. Design: Retrospective analysis of a single-institution retinal imaging dataset using adversarial machine-learning experiments. Participants: A total of 4,271 patients contributing 13,959 CFPs from Massachusetts Eye and Ear. Methods: Vision Transformer (ViT) was trained for glaucoma detection and for prediction of race, sex, and ethnicity. Standard and disease-aware (DA) variants of four adversarial attacks--Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini & Wagner (C&W), and a diffusion-based attack--were applied to suppress demographic prediction; DA attacks augmented the adversarial objective with a disease-preservation term. Cross-architecture transferability was assessed by generating perturbations on ViT and applying them to ResNet50 and EfficientNetB0. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) and accuracy for glaucoma and demographic classification before and after perturbation, and disease-preservation and attack transferability across architectures. Results: At baseline, CFPs encoded both glaucoma-related and demographic information. Glaucoma detection AUCs were 0.958 (95% CI, 0.949-0.967), 0.960 (95% CI, 0.951-0.967), and 0.963 (95% CI, 0.955-0.971) in the race, sex, and ethnicity analysis cohorts, respectively. Demographic prediction performance was also high, with AUCs of 0.955 (95% CI, 0.945-0.963) for race, 0.983 (95% CI, 0.977-0.988) for sex, and 0.992 (95% CI, 0.987-0.996) for ethnicity. Standard attacks substantially reduced demographic AUC but often degraded glaucoma detection. Disease-aware optimization improved disease preservation while maintaining demographic suppression. Using a prespecified success criterion of at least 90% disease AUC preservation and demographic AUC reduction to 30% or less of baseline, DA-PGD and DA-Diffusion succeeded across race, sex, and ethnicity; DA-C&W succeeded for sex and ethnicity. Cross-architecture transferability experiments demonstrated that disease preservation transferred more robustly than demographic suppression. Conclusions: Disease-aware adversarial perturbations reduced the recoverability of demographic information in CFPs under white-box conditions while preserving glaucoma-relevant features, suggesting these representations are partially separable. Reduced demographic recoverability did not fully transfer across architectures, highlighting the need for architecture-agnostic methods.

Spinal cord injury (SCI) causes rapid and severe bone loss in the paralysed lower limbs, particularly at the distal femur and proximal tibia, where fragility fracture risk is high. In vitro nanoscale vibration at 1 kHz has been shown to promote osteogenic differentiation and inhibit osteoclastogenesis, suggesting potential as a targeted mechanical intervention. This study aimed to develop and evaluate a wearable device for delivering and monitoring localised nanovibration at the distal femur in individuals with SCI. The device delivered continuous sinusoidal nanoscale stimulation at 1 kHz via a bone-conduction transducer, with an opposing accelerometer used to monitor transmitted vibration in real time. Design and target-site selection were refined through two healthy-volunteer investigations comparing the distal femur, proximal tibia, and distal tibia. Bovine femur experiments characterised vibration transmission under controlled benchtop conditions. Preliminary repeated-use feasibility was assessed in one individual with motor-complete SCI. Healthy volunteer testing showed that although the ankle initially produced the highest transmitted amplitudes, these were highly variable, and positioning was inconsistent. Within the knee region, the distal femur provided the most practical and repeatable site for a wearable application. In bovine femur experiments, scanning laser vibrometry demonstrated measurable vibration on the condylar surface opposite the transducer, and depth-resolved measurements confirmed that nanoscale vibration remained detectable within bone. A gel interface layer reduced the transmitted amplitude. In the feasibility evaluation, 61 sessions were completed over 14 weeks, with logged accelerometry confirming repeated nanoscale vibration transmission. These findings establish feasibility and support further device optimisation and translational studies.

ABSTRACTThe repair of large bone defects remains a major clinical challenge, in part due to inadequate vascularization and poor integration of graft materials. Tissue engineering strategies that recapitulate the developmental process of endochondral ossification, whereby a cartilage template remodels into bone, have shown significant potential in pre-clinical models of large bone defect healing. However, successfully scaling these approaches to clinically relevant sizes will require the development of strategies to support the rapid vascularization of the graft following implantation in vivo. Here, mechanically reinforced templates were first fabricated by integrating hypertrophic cartilage microtissues derived from human mesenchymal stem/stromal cells (MSCs) within an osteoconductive 3D-printed polycaprolactone (PCL) framework coated with nano-hydroxyapatite (nanoHA). In vitro the cartilage microtissues fused and generated an extracellular matrix rich in sulphated glycosaminoglycans and collagen. To prevascularize these constructs, vascular microtissues derived from a co-culture of endothelial cells and MSCs were incorporated into a central channel within the construct, which generated a microvascular network within the graft in vitro. Following subcutaneous implantation, hypertrophic cartilage templates with ( vascular-channel group) and without ( empty-channel group) this central vascularized channel supported endochondral bone formation. Quantitative microCT and histological analyses revealed significantly greater remaining bone in the empty-channel group, whereas the vascular-channel group supported enhanced vascularization and remodeling of the graft in vivo. These findings support the continued development and testing of a modular biofabrication strategy that combine self-organizing hypertrophic cartilage and vascular microtissues with osteoconductive 3D-printed architectures to generate scalable, prevascularised hypertrophic cartilage templates for endochondral bone repair. Key-words: spheroids, microtissues, hypertrophic cartilage, vascularization, endochondral ossification, bone tissue engineering.

Minimally invasive surgery is a powerful technique that enables operations deep within the body while minimizing patient trauma and recovery time. Optical endoscopes are key to providing intraoperative vision but still face challenges due to the loss of essential senses, including depth perception and tactile feedback for tissue evaluation. Thus, it is critical to develop endoscopic imaging technologies that can augment operators with critical information. In this work, we explore a prototype multimodal 3D imaging endoscope that integrates volumetric light-field imaging with laser-speckle contrast imaging to simultaneously capture 3D structure and blood-flow information in a clinically relevant form factor.

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

Objective: To develop and externally validate a simple antepartum cumulative risk score that stratifies both vaginal birth after cesarean (VBAC) success and neonatal morbidity among patients undergoing trial of labor after cesarean (TOLAC). Methods: This retrospective cohort study was conducted in 2 stages: model development in a single tertiary care center in California (2019 to 2025) and external validation in the National Vital Statistics System natality files (2020 to 2024). The derivation cohort included 1,418 TOLAC attempts; the national validation cohort included 477,693 TOLAC attempts. A point-based score was constructed from routinely available antepartum characteristics associated with VBAC. VBAC success and neonatal intensive care unit (NICU) admission were evaluated across score levels in both cohorts, and model discrimination was assessed using area under the receiver operating characteristic curve (AUC). Results: In the derivation cohort, 1,087 of 1,418 patients (76.7%) achieved VBAC. The logistic regression model showed reasonable discrimination (AUC 0.70, 95% CI 0.67-0.73). VBAC success declined from 89.1% at a score of -1 to 37.8% at scores of 4 or higher, whereas NICU admission increased from 31.7 to 200.0 per 1,000. Uterine rupture occurred in 28 of 1,418 TOLAC attempts (1.97%) and was not predicted by antepartum characteristics. In the national cohort, VBAC success similarly declined from 90.5% to 44.8%, whereas NICU admission increased from 43.8 to 111.1 per 1,000 across the same score range. Conclusion: A simple antepartum risk score stratified both VBAC success and neonatal morbidity in single-center and national TOLAC cohorts, supporting its potential use in patient-centered counseling.

Urinary stone disease is a common urological condition with increasing incidence, particularly in developed countries. Laser lithotripsy (LL) has become a preferred minimally invasive treatment due to its high precision and low tissue damage. Recent studies suggest that cavitation plays a critical role in stone damage during LL, and three-dimensional passive cavitation mapping (3D-PCM) has emerged as a promising tool for detecting these events. However, clinical translation of 3D-PCM remains challenging due to limitations in imaging depth, field of view (FOV), and procedural compatibility. Here, we present a large-FOV dual-modality imaging system (3D-PCM and B-mode ultrasound) based on a large-aperture planar ultrasound array. Through array optimization and model-based reconstruction, our system achieves an expanded FOV of ~40*40mm^2 at a clinically relevant imaging depth of ~110mm, while maintaining high spatial resolution of ~0.6 mm laterally and ~0.4 mm axially. In vivo experiments in a porcine model demonstrate that the reconstructed cavitation distribution correlates well with stone damage. Our technology has the potential to provide real-time treatment feedback during LL without disrupting the standard workflow.

Purpose: To assess the feasibility of quantitative fundus autofluorescence (FAF) measurement in early age-related macular degeneration (AMD) using the freely available ImageJ software, to characterize signal intensity across FAF patterns, and to evaluate interobserver reproducibility in pattern classification. Methods: Single-center, non-blinded, retrospective, consecutive-case analytical study. FAF images acquired with Spectralis OCT+HRA (Heidelberg Engineering) from patients with early dry AMD seen at a tertiary referral center between January 2010 and September 2016 were analyzed. A standardized 300x300-pixel region of interest (ROI) centered on the fovea was evaluated in ImageJ v2.0.0-rc54/1.51h (Fiji distribution). Mean, minimum, and maximum autofluorescence (AF) pixel intensity were recorded. Each image was independently classified according to the Bindewald classification system by two graders; a third senior grader adjudicated discordances. Cohen's kappa (k) was used to assess interobserver agreement. Results: Of 423 patients with available FAF studies, 107 had dry AMD; 45 met quality and diagnostic criteria for early AMD and were included in the quantitative analysis. Mean age was 73.47 +/- 8.1 years; 62.2% were female. Mean FAF intensity was 120.26 (range 74.76-160.79); mean minimum was 32.07 (range 3-63) and mean maximum was 205.80 (range 125-255). Seven of eight Bindewald patterns were identified; the stippled pattern was absent. The most frequent pattern was minimal changes (31.1%), followed by increased focal (24.4%) and patchy (15.6%). Reticular pattern showed the highest mean AF (143.8), while lace pattern showed the lowest (88.4). Interobserver agreement for Bindewald pattern classification was almost perfect (k = 0.969; 95% CI, 0.908-1.000; p < 0.001). Agreement for lesion extent was moderate (k = 0.531) and for foveal involvement was substantial (k = 0.622). Conclusions: Quantitative FAF evaluation of early AMD using ImageJ is feasible and reproducible. ImageJ represents a cost-free alternative for multimodal retinal image analysis, with potential for automated screening applications in resource-limited settings. Keywords: age-related macular degeneration; fundus autofluorescence; ImageJ; quantitative autofluorescence; image analysis; Bindewald classification; interobserver agreement

Subretinal photovoltaic implants provide central vision to patients blinded by atrophic age-related macular degeneration, with acuity limited by their 100-{micro}m pixels. Higher resolution requires smaller pixels incorporating three-dimensional electrodes, which can be fabricated by gold electroplating. However, the retinal response to exposed gold remains poorly characterized. Here, we evaluated gold biocompatibility on subretinal implants in Royal College of Surgeons rats and compared it with platinum- and titanium-coated surfaces. Although in-vivo optical coherence tomography revealed no overt structural disruption, gold implants induced cellular-scale anomalies, including abnormal morphology of rod bipolar cells, microglial accumulation near the implant, and increased cell death within days after implantation. These effects occurred across flat, pillar, and honeycomb geometries, indicating a material-rather than geometry-dependent response. By contrast, platinum- and titanium-coated implants showed substantially lower loss and morphological disruption of rod bipolar cells, together with markedly reduced microglial activation. These findings indicate that exposed gold surfaces can induce acute retinal inflammation and neuronal loss, whereas conformal platinum or titanium coatings substantially improve biocompatibility. Such coatings enable the development of three-dimensional subretinal prostheses with smaller pixels for improved visual resolution.