Bioengineering

Objective: Current guidelines recommend different adjuvant treatment strategies for women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. We aimed to evaluate recurrence patterns and explore the clinical implications for adjuvant therapy in this population. Materials and methods: This multicenter retrospective study included 86 patients with endometrioid, p53 abnormal, FIGO stage I, II endometrial cancer treated between 2013 and 2023 at six hospitals in the Republic of Korea. Clinicopathologic data were collected through medical record review. Progression free survival (PFS) was estimated using the Kaplan Meier method, and prognostic factors were analyzed using Cox proportional hazards regression models. Results: The 12, 24, and 36 month PFS rates were 91%, 78%, and 76%, respectively. Among the 16 patients who experienced recurrence, 12 (75%) had distant metastases. Adjuvant chemotherapy showed a trend toward improved PFS compared with adjuvant radiation therapy with or without chemotherapy (p=0.10). In multivariable analysis, the p53 overexpression pattern was independently associated with worse PFS compared with the p53 null pattern (HR 8.59, 95% CI 1.61 to 45.79, p=0.01). Conclusion: Given the poor PFS, the predominance of distant recurrence and the observed trend toward improved PFS with chemotherapy, adjuvant chemotherapy may be considered in women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. Further studies are warranted to clarify the prognostic impact of p53 abnormality subtypes.

Objective: The aim of the current study was to evaluate the feasibility of a multi-channel distributed wearable ultrasound (D-WUS) system capable of simultaneously probing dynamic muscle activity across multiple anatomical sites to assess neuromuscular coordination during physical activity. Methods: Twenty NCAA Division I women athletes (8 with prior knee injury) performed countermovement jumps (CMJs) on dual force plates while wearing a four-channel D-WUS system targeting the vastus lateralis (VL) and vastus medialis oblique (VMO) in both limbs. Real-time M-mode ultrasound data were processed using a composite factorization approach integrating nonnegative matrix factorization and principal component analysis (CF-PCA) to extract dominant muscle-contraction patterns. Correlation and timing metrics between VL and VMO were compared across injury status. Results: CMJ performance metrics (jump height, RSImod) did not differ between groups (p > 0.05). However, D-WUS revealed significant differences in muscle coordination in the right limb: VL-VMO correlation (p = 0.029) and timing symmetry (p = 0.019) were reduced in participants with prior knee injury; left-limb differences were not significant. Right-limb timing symmetry was negatively correlated with SMFA score (rho = -0.48, p = 0.033). Functional survey scores (SMFA) also differed significantly (p = 0.001), while VISA-P scores did not. Conclusion: These preliminary findings provide feasibility-level evidence that distributed wearable ultrasound can detect limb-specific neuromuscular coordination differences not reflected in force-plate biomechanics, motivating larger prospective studies to establish reliability and clinical utility. Significance: With further validation, this approach may offer a scalable, objective means of monitoring muscle contraction during rehabilitation.

Neurologic autoantibody cell-based assay images are difficult to interpret when fluorescence is weak, focal or dominated by background signal. We developed a visual artificial intelligence overlay that transfers detected cell boundaries from the structural channel to the antibody-staining image and evaluated standalone model concordance and its association with reader classifications. The analysis used 2 763 records for model development and 499 held-out records spanning 14 antibody targets. Four readers assessed the held-out records first without assistance and then with the boundary overlay and model category available. The selected model agreed with the reader-derived three-class reference in 85.2% of records, but recall was lowest for weak-positive or equivocal records. Reader agreement increased in the assisted session, and almost all revised ratings moved towards the model category. The combined display may support consistent visual review while also creating a risk of model-directed anchoring; its role as a second-read aid requires independent validation.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

Purpose: To characterize the biomechanical response of the optic nerve head (ONH) in myopic versus non-myopic individuals with normal-tension glaucoma (NTG), and to evaluate the association between ONH strains and the rate of visual field (VF) progression. Design: Retrospective clinical cohort study. Participants: A total of 118 NTG eyes from 77 patients with [&ge;] 2 years of follow-up and 5 VF tests were enrolled, including 66 eyes with myopia and 52 eyes without. Methods: Each participant underwent three-dimensional (3D) optical coherence tomography (OCT) imaging of the ONH under three mechanical loading conditions: 20{degrees} adduction, 30{degrees} abduction and acute intraocular pressure (IOP) elevation by applying a 0.80 N force to the lower eyelid. Effective strains across the global ONH complex and within specific segmented tissues (pre-lamina, lamina cribrosa, choroid, and sclera) were quantified using a validated 3D strain-mapping algorithm. Results: Myopic NTG eyes exhibited significantly higher ONH strains compared to non-myopic eyes under acute IOP elevation (10.1{+/-}2.7% vs. 9.3{+/-}1.8%, P = 0.044) and abduction (9.2{+/-}2.3% vs. 8.4{+/-}2.1%, P = 0.048). In the non-myopic group, ONH strains varied significantly across conditions (P < 0.001), with adduction (9.9{+/-}2.0%) and acute IOP elevation (9.3{+/-}1.8%) eliciting greater strains than abduction (8.4{+/-}2.1%). These condition-dependent variations were less pronounced in the myopic group (10.1{+/-}2.7% vs. 9.8{+/-}2.7% vs. 9.2{+/-}2.3%, P = 0.07). Multivariable linear mixed-effects analysis revealed that in myopic NTG, higher ONH strains induced by adduction were significantly associated with faster MD decline (estimate: -0.139 dB/year per 1% increase, P = 0.03, adjusted for age, spherical equivalent and baseline perimetric mean defect). Conversely, larger strains during acute IOP elevation were associated with slower progression (estimate: 0.184 dB/year per 1% increase, P = 0.004). Tissue-specific analysis confirmed that adduction-induced strains in the choroid and pre-lamina tissue as well as IOP-induced strains across all ONH tissues were significantly correlated with the rate of perimetric progression in myopic eyes (all P < 0.05). Conclusions: In this cohort, greater adduction-induced ONH strain was associated with faster perimetric deterioration in myopic NTG. These findings support further investigation of gaze-related optic nerve traction as a potential non-IOP-related biomechanical factor in myopic NTG. Keywords: Normal tension glaucoma, Myopia, Optic nerve head, Ocular biomechanics, Visual field Progression, adduction.

Aims: Myocardial strain enables sensitive assessment of cardiac function, but its clinical application has largely focused on individual chambers or selected patient populations. We evaluated the feasibility and clinical relevance of fully automated multi-chamber strain assessment in consecutive patients undergoing routine transthoracic echocardiography (TTE). Methods and results: This retrospective study included 8,350 consecutive adults undergoing TTE. A hospital-wide artificial intelligence (AI)-based system automatically measured left ventricular (LV) global longitudinal strain (LVGLS), left atrial (LA) reservoir strain (LASr), and right ventricular (RV) free-wall strain (RVFWS). AI-derived LVGLS was available from at least one apical view in 97.1% of patients and from all three views in 94.8%; LASr and RVFWS were available in 94.1% and 94.5%, respectively, resulting in complete multi-chamber strain assessment in 90.3%. Strain measures showed modest correlations with conventional chamber-specific echocardiographic parameters and differential associations with clinical outcomes. Gaussian mixture modeling identified three distinct clusters characterized by preserved multi-chamber function, RV-dominant dysfunction, and pan-chamber dysfunction. Compared with the preserved cluster, pan-chamber dysfunction was associated with the highest risk of all-cause death or heart failure hospitalization (adjusted hazard ratio, 3.44; 95% CI, 2.43-4.88; P<0.001). Prognostic associations remained evident in patients with LV ejection fraction (LVEF) [&ge;]50%, and cluster assignment provided incremental prognostic information beyond clinical factors, LVEF, and RV fractional area change. Conclusions: Fully automated multi-chamber strain assessment was feasible in a large consecutive TTE cohort. Integrating strain across cardiac chambers may provide complementary functional characterization and identify clinically relevant clusters with prognostic information beyond conventional assessment.

Background: Transthoracic echocardiography (TTE) remains the gold standard for assessing left ventricular (LV) diastolic dysfunction (DD), but noninvasive alternatives are needed to overcome geometric and acoustic limitations. 4D-Flow MRI enables comprehensive assessment of intraventricular pressure differences (IVPDs) and flow propagation velocity (VP). We aimed to evaluate 4D-Flow-derived IVPDs and VP in patients with hypertrophic cardiomyopathy (HCM). Methods: Thirty-eight HCM patients and 38 age- and sex-matched healthy controls underwent TTE and 4D-Flow MRI. Spatiotemporal maps of velocity and IVPDs, decomposed into inertial and convective components, were derived along the main inflow streamline from the mitral valve to the apex. Peak early filling rate (PEFR) was used to normalize IVPDs and VP. Results: While absolute peak flow rates were comparable, HCM patients exhibited significantly faster VP (100.4 vs. 60.2 cm/s, p < 0.01) and higher positive early E IVPD (1.40 vs. 0.92 mmHg, p < 0.01) compared to controls. Normalizing IVPDs by PEFR amplified group differences, revealing a markedly increased flow-normalized E-IVPD (5.13 vs. 2.94 x 103 mmHg.ml1.s, p < 0.001) and deeper reverse ER IVPD (-6.06 vs. -4.61 x 103 mmHg.ml1.s, p < 0.01) in HCM. These differences were primarily driven by the inertial components. Multivariable analysis demonstrated that ER IVPD is independently determined by PEFR and the TTE E/E' ratio (r = 0.65, p < 0.001). Conclusions: 4D-Flow MRI-derived, flow-normalized IVPDs and propagation velocity provide robust, noninvasive markers of altered LV chamber stiffness and impaired diastolic filling in HCM, offering deeper 3D hemodynamic insights into diastolic dysfunction.

Chinese is one of the living languages with the highest homophone densities, making homophone processing a critical component of language comprehension and orthographic decoding, as well as an ideal model for examining its cognitive mechanisms. This study assessed mono- and bi-character homophone selection function in 129 Mandarin- and Cantonese-speaking participants, including 66 cognitively healthy individuals, 17 with semantic variant (sv) primary progressive aphasia (PPA), 23 with the nonfluent/agrammatic variant (nfv) PPA, and 23 with the logopenic variant (lv) PPA. All PPA variants showed impairment in homophone selection, with most significant deficits in svPPA and lvPPA. ROC analyses demonstrated its strong diagnostic utility for distinguishing PPA from controls. Voxel-based morphometry revealed that homophone performance correlated with gray-matter volume in left anterior and inferior temporal regions, with variant-specific patterns observed. These findings described the potential role of homophone selection in diagnosing PPA syndrome in Chinese speakers, illuminate crosslinguistic influences on language impairment in PPA, and provide novel insights into the cognitive processing of homophones.

Purpose: To evaluate the associations of the numerical AI-CO center index and visually classified central dark-area extent with Humphrey 10-2 mean deviation (MD). Methods: This single-center retrospective study included 320 eyes of 169 patients with AI-CO imaging, broad-field 24-2F or 30-2 testing, and 10-2 testing. Both visual field examinations were performed within 365 days before or after AI-CO imaging. Two ophthalmologists independently classified central dark-area extent into five Levels (0-4). Analyses assessed Spearman correlations, areas under the receiver operating characteristic curve (AUCs), distributions across final central dark-area Levels, and interrater reliability. Patient-cluster bootstrap confidence intervals and a one-eye-per-patient sensitivity analysis addressed inclusion of both eyes. Results: AI-CO center correlated with 10-2 MD (rho=0.829; 95% CI, 0.773-0.873). AUCs for 10-2 MD below -2, -6, and -12 dB were 0.902, 0.939, and 0.961, respectively. The numbers of eyes in Levels 0-4 were 66, 57, 94, 53, and 50; median 10-2 MD values were -0.59, -1.48, -3.54, -6.24, and -18.15 dB, respectively. Quadratic-weighted kappa was 0.970 (95% CI, 0.958-0.981). Sensitivity analyses yielded broadly similar findings. Conclusions: The numerical AI-CO center index and visually classified central dark-area extent were associated with central visual field damage. Assessing the extent of dark regions in the central image alongside the numerical index may provide useful information for interpreting central visual field status from the AI-CO display.

Gait impairments are a major contributor to disability in patients with Parkinson's disease despite dopaminergic and neuromodulation therapies. External cues (e.g., visual targets, rhythmic auditory sounds) are commonly used to improve gait quality and reduce freezing of gait (FOG), yet their effectiveness varies across patients and contexts. Improved understanding of the neural mechanisms of cueing benefits is essential to personalize rehabilitation strategies and improve outcomes. We use mobile electroencephalography (EEG) to test the hypothesis that cues improve gait through engagement of voluntary motor control mechanisms to compensate for impaired gait automaticity. In addition, we investigate whether this mechanism is related to cueing benefits or freezing status. Eighteen PD patients with a history of freezing (PD-F), 21 patients without freezing (PD-NF) and 19 age-matched healthy controls (HC) walked overground under three conditions: normal walking, synchronizing to auditory cues, and stepping over visual cues. Stride-locked beta band (13-30 Hz) power dynamics in sensorimotor cortex were used to measure cortical control of stepping. Spatiotemporal gait parameters improved with cues in all groups. However, PD-F had smaller cue-related gains in speed and stride length when compared with PD-NF for both cue types, and did not improve gait speed with visual cues. EEG revealed greater contralateral sensorimotor cortex swing-phase beta suppression in PD-F than PD-NF patients, with visual cues eliciting the most robust response. In contrast, beta suppression with cues was less pronounced in PD-NF and HC, despite greater behavioral gains. In addition, engagement of sensorimotor cortex during visual cueing was associated with divergent gait effects across PD subgroups: In PD-NF greater beta suppression was associated with less gait improvement, whereas in PD-F the opposite was observed. These findings suggest distinct sensorimotor control mechanisms for cue utilization in PD-F and PD-NF, and indicate that engagement of voluntary motor control circuits may underlie cueing benefits primarily in PD-F.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Over 30% of patients with epilepsy are refractory to medication and may require surgical or neuromodulation-based intervention. Accurate localization of seizure-onset zones (SOZ) and assessment of treatment-associated brain responses remain major clinical challenges. Current workflows may involve invasive intracranial monitoring, prolonged hospitalization, adjustment of anti-seizure medications, and seizure capture to localize the SOZ, while opportunities for complementary intraoperative functional assessment remain limited. We used intraoperative functional ultrasound imaging (fUSI) to characterize interictal (i.e., non-seizure) cortical activity in nine patients with drug-resistant focal epilepsy and to assess treatment-associated brain responses following SOZ resection and responsive neurostimulation (RNS). fUSI signals were acquired directly from the exposed cortex during standard-of-care surgery. We developed six ultrafast power Doppler-based interictal activity markers (IaMs) to distinguish SOZ from peri-resection zone (PRZ) cortex and to quantify treatment-associated changes. The IaMs revealed distinct interictal hemodynamic dynamics between SOZ and PRZ cortex. Following SOZ resection, all six markers changed significantly within the surrounding PRZ. RNS also induced robust, stimulation paradigm-dependent modulation of cortical hemodynamic dynamics. Notably, RNS and surgical resection produced concordant directional changes across five of the six IaMs, despite representing fundamentally different therapeutic interventions. Together, these findings demonstrate the feasibility of intraoperative fUSI for characterizing and distinguishing interictal dynamics within SOZ and PRZ cortex and assessing treatment-associated cortical responses during human epilepsy surgery. Furthermore, fUSI could provide a complementary high-resolution functional imaging approach alongside established clinical methods for SOZ localization and intraoperative functional assessment.

Background and Objectives Transient ischemic attack (TIA) diagnosis is challenging because it relies mainly on clinical assessment and exclusion of common differential. Functional MRI (fMRI) may provide a diagnostic biomarker by detecting transient brain dysfunction not visible on conventional imaging. This pilot study aimed to characterize acute motor-task BOLD activation in patients with suspected DWI-negative TIA and compare functional patterns between confirmed TIA and alternative diagnoses. Secondary objectives were to assess longitudinal changes in activation and connectivity at 3 months. Methods In this prospective single-center study, adults presenting within 24 h of transient anterior-circulation neurological symptoms underwent conventional MRI and additional motor-task fMRI. Patients with acute DWI lesions were excluded. Final diagnosis was established after 3-month follow-up. Whole-brain analyses, individualized motor cortex region-of-interest (ROI) analyses, and generalized psychophysiological interaction (gPPI) connectivity analyses were performed during the acute phase and at 3 months. Results Eight patients were enrolled; six had complete longitudinal imaging data and were included in the final analyses. Final diagnoses were TIA (n=2), migraine aura (n=2), functional neurological disorder (n=1), and hypoglycemia (n=1). Whole-brain and ROI analyses showed reduced bilateral motor-related BOLD responses during the acute phase in both TIA patients and in the patient with hypoglycemia. In contrast, patients with migraine and functional neurological disorder exhibited less marked longitudinal changes. In TIA patients, motor cortex activation increased at 3 months, tending to normalize toward values observed in non-TIA participants. Connectivity analyses revealed heterogeneous patterns, but the 2 TIA patients demonstrated increased longitudinal connectivity between the motor cortex contralateral to symptoms and other sensorimotor regions, including supplementary motor, premotor, parietal, and postcentral cortices. Discussion These preliminary findings suggest that TIA may be associated with transient bilateral reductions in motor-network activity. Functional abnormalities appeared to improve over time, supporting the hypothesis of reversible network dysfunction. Although limited by the very small sample size, this study provides the first longitudinal assessment of emergency task-based fMRI in rigorously defined TIA and supports further investigation of acute functional MRI as a potential biomarker of transient cerebral dysfunction.

Syntax processing impairment is a defining feature of primary progressive aphasia (PPA), particularly in the nonfluent/agrammatic variant. However, current models are derived largely from Indo-European languages, and it remains unclear whether the cognitive and neuroanatomical mechanisms of syntax processing generalize to Chinese, an analytic, pro-drop language that encodes grammatical relationships primarily through word order and function words rather than inflectional morphology. Participants were recruited from the international Chinese Language Assessment in PPA cohort and included 64 individuals with PPA (19 logopenic, 15 nonfluent/agrammatic, 15 semantic, and 15 unclassifiable/mixed) and 60 cognitively healthy controls. Participants completed sentence comprehension and production tasks with systematically manipulated syntactic features. Item-level linguistic determinants were examined using ridge regression, diagnostic performance using receiver operating characteristic analyses, and neuroanatomical correlates using voxel-based morphometry of structural MRI. Performance on sentence comprehension and production was influenced by grammatical and lexical features. Subject-relative clauses, passive constructions, a higher proportion of function words, longer character length, and greater lexical content were associated with poorer performance among participants with PPA. Sentence comprehension was impaired in the logopenic, nonfluent/agrammatic, and semantic variants, even when isolated to the syntactic component of the sentence comprehension task. Similarly, sentence production was impaired across all PPA variants (all P < 0.01 versus controls). No significant differences were observed among PPA variants. Adjusted receiver operating characteristic analyses demonstrated high discrimination between each PPA variant and controls, with areas under the curve ranging from 0.87 to 0.97 for sentence comprehension-syntax and from 0.91 to 0.97 for sentence production. However, pairwise comparisons showed no significant differences in discriminative performance among PPA variants after correction for multiple comparisons. Both sentence comprehension-syntax and sentence production showed associations with semantic and repetition measures in addition to motor speech measures. Voxel-based morphometry showed that higher sentence comprehension-syntax scores were associated with greater grey matter volume in left frontotemporal and cingulate regions, whereas higher sentence production scores were additionally associated with greater gray matter volume in frontostriatal structures, including the caudate, putamen, and thalamus. Syntactic processing impairment in Chinese-speaking individuals with PPA extends beyond the nonfluent/agrammatic variant and is associated with distributed frontotemporal and frontostriatal regions, as well as lexical-semantic and auditory verbal short term memory mechanisms. These findings suggest that the cognitive and neural manifestations of syntactic impairment may be shaped by language-specific typological properties, underscoring the need for linguistically informed approaches to the assessment and interpretation of PPA.

Introduction: Cognitive intraindividual variability (IIV), a measure of fluctuation in performance across time or tasks, is an emerging marker of cognitive decline. However, little is known about within-domain IIV in Latino adults. To address this gap, we examined memory IIV and hippocampal integrity in older Latino individuals. Methods: Sixty-seven participants from the Boston Latino Aging Study underwent cognitive testing and MRI. Linear regressions assessed whether memory IIV was associated with hippocampal volume and hippocampus-cingulum tract fractional anisotropy. Additional analyses evaluated associations using mean memory performance as the outcome. Results: Higher mean performance was associated with greater hippocampal volume (=0.24, p=.03), while memory IIV was not (=0.028, p=.84). Higher memory IIV was associated with lower fractional anisotropy in the hippocampus-cingulum tract (=-0.34, p<.01), while mean performance was not (=-0.07, p=.52). Discussion: Memory IIV may be more closely linked to hippocampal white matter, while mean memory performance may better reflect hippocampal volume.

Background: Anatomic isthmus 3 (AI 3) is defined as the myocardium between the pulmonary valve annulus and the ventricular septal defect patch that persists after tetralogy of Fallot (ToF) repair. Ventricular tachycardia (VT) propagated through slow conducting AI 3 (SCAI 3) represents a significant cause of morbidity in ToF. There is an unmet need for non-invasive identification of SCAI 3 in patients with ToF. Objectives: To assess whether the anatomic features of AI 3 as derived from cardiac computed tomography (CT) can predict slow conduction velocity during EP study. Methods: This retrospective single-center study included patients with ToF who underwent an electrophysiologic study and had AI 3 width measured from the pre-procedure cardiac CT. All patients underwent programmed ventricular stimulation, and electroanatomic. Logistic regression was performed to assess the relationship between AI 3 width and the presence of SCAI 3. Results: Of 27 consecutive patients with ToF, twelve patients were found to have SCAI 3, and ten patients were found to have SCAI 3 mediated VT. The AI 3 was significantly narrower in patients with SCAI 3 compared to those with normal AI 3 conduction (18.4 +/- 6.6 mm vs. 29.2 +/- 9.0 mm; p = 0.001). An AI 3 width above 21.5 mm carried a negative predictive value of 80.0%. Conclusion: In adults with repaired ToF referred for electrophysiology study, a narrower CT-derived AI 3 width was associated with SCAI 3 identification. AI 3 width measured on a CT obtained before PV replacement could help guide invasive risk stratification.

Background and Objective: The volume of patient portal messages has grown substantially and artificial intelligence (AI) can support clinical staff and improve the timeliness of patient care. Our objective is to test if an AI tool for managing the electronic inbox In-Basket reduces response times to patient portal messages in neurology. Methods: We conducted an implementation study from January to May 2026 in the neurology department at two clinical sites within a regional medical system. A single AI tool was implemented to auto-categorize and auto-route all patient messages received in the electronic inbox. The tool also generates optional draft replies to messages. Primary process measures were the median weekly time (hours) from message creation to the first action and to closure. Impact was analyzed using a controlled interrupted time-series (ITS) design, comparing message-volume-matched control divisions. Results: Among 24,142 messages analyzed, pre-intervention median weekly time to first action was 11.3 hours [IQR: 5.6, 15.1] (Site A) and 16.1 hours [IQR: 13.8, 19.0] (Site B), falling post-intervention to 1.0 hour [IQR: 0, 3.1] (Site A) and 0.6 hours [IQR: 0.2, 2.4] (Site B). Controlled interrupted time-series analysis showed a significant immediate differential level change in median weekly hours to first action at Site A (-92.5%, 95% CI: -97.5% to -78.0%), and a significant downward trend at Site B (-43.5%, 95% CI: -65.2% to -8.2%). Changes in time to closure were directionally favorable but not statistically significant at either site. There was low use of the generated draft replies, only 0.5% at Site A and 3% at Site B. Discussion: Implementation of AI-assisted message categorization and routing was associated with a substantially reduced initial action time for patient In-Basket messages in Epilepsy. The utilization of AI-generated draft-replies to messages was low. These findings support the feasibility and effectiveness of AI-augmented workflows to improve inbox management.

Purpose: To compare the performance of three large language models (LLMs), ChatGPT-o1, Grok 3, and Gemini 2.0 Pro, on the American College of Prosthodontists' National Prosthodontic Resident Examination (NPRE). Materials and Methods: Official NPRE examinations from years 2015-2024 were retrieved from the American College of Prosthodontists website and used to prompt each LLM. Paid access tiers and advanced reasoning were enabled. Responses from LLMs were recorded and compared against the official answer keys for accuracy, variability across years, and consistency of responses. Duplicate questions were retained for the primary analysis and removed in a sensitivity analysis. Accuracy (percent correct) was compared using generalized estimating equations binary logistic regression (primary); Cochran's Q with Bonferroni-adjusted McNemar tests (sensitivity). Year-to-year variability (SD of annual accuracy) was compared using weighted Levene tests. Consistency across duplicate items was quantified as the percentage of perfect matches and compared using Cochran's Q with Bonferroni-adjusted McNemar tests. Results: Across 1,487 questions, overall accuracy differed among models (global P < 0.001). ChatGPT-o1 achieved 78.4% correct, Grok 3 76.4%, and Gemini 2.0 Pro 73.2%; ChatGPT-o1 and Grok 3's accuracy significantly exceeded Gemini 2.0 Pro (both P [&le;] 0.016), with no significant difference between ChatGPT-o1 and Grok 3 (P = 0.099). After removing duplicates (n = 1,200), results were similar: ChatGPT-o1, Grok 3, and Gemini 2.0 Pro were 77.1%, 76.6%, and 71.5% correct, respectively; ChatGPT-o1 and Grok 3's accuracy was significantly higher than Gemini 2.0 Pro (both P < 0.001). Variability across years did not differ significantly (P [&ge;] 0.097). Grok 3 showed significantly higher perfect-match consistency (79.2%) than ChatGPT-o1 (69.4%) and Gemini 2.0 Pro (64.5%) (P [&le;] 0.010). Conclusions: Contemporary LLMs showed 70-80% accuracy on postgraduate prosthodontic multiple-choice content. While promising for educational support, residual error and inconsistency warrant human oversight and transparent methods reporting before curricular adoption.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.

Stroke is a leading cause of mortality and disability in Uganda, with a prevalence of approximately 2.5% and a 25-30% 30-day mortality rate, exacerbated by limited access to predictive tools and advanced diagnostics. Machine learning (ML) offers potential for early stroke risk stratification, but its application in resource-constrained settings like Uganda requires tailored models and rigorous performance evaluation, as well as assessment of misclassification patterns. This study aimed to develop and internally validate an ML-based predictive model for stroke using clinical and socio-demographic characteristics among patients at Mulago National Referral Hospital (MNRH) and residents of Wakiso District, Uganda, identify key predictors and assess misclassification patterns to enhance targeted prevention. A retrospective case-control study utilized secondary data from 1,016 participants (254 stroke cases, 762 controls) across three prior studies conducted between 2006 and 2015. Eight ML algorithms were evaluated using 5-fold cross-validation, with performance metrics including accuracy, sensitivity, positive predictive value, F1-score, and area under receiver operator characteristics curve (AU-ROC). McNemars test compared model classification performance of the top 2 models. Feature importance identified key predictors, and misclassification patterns were analyzed using classification matrix and line graphs. The Random Forest (RF) model achieved the highest performance (95.18% accuracy, 90.10% F1-score, 97% AU-ROC). McNemars test confirmed RFs superior classification (p=0.01). Age, systolic blood pressure, total cholesterol, and waist-hip ratio were the top drivers of RF stroke predictions. RF misclassified 8.3% of stroke cases and 3.3% of non-stroke cases, due to overlaps in risk profiles (e.g., lower cholesterol, systolic blood pressure and age in false negatives). The RF model demonstrates strong potential for stroke risk prediction in Uganda, leveraging routine data to prioritize high-risk individuals. Misclassification analysis highlights areas for model refinement, supporting its potential for integration into clinical workflows for early intervention and stroke prevention in resource-limited settings. Future external validation across diverse populations is recommended.