Annals of Biomedical Engineering

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.

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.

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.

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.

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.

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.

Background: School-based implementation of robot-assisted walking may support increased physical and social participation for children with physical disabilities, but evidence from an adequately powered trial is needed. This pilot study evaluated implementation strategies and study procedures to inform a future definitive trial. Methods: Participants were randomized to 12-weeks of robot-assisted walking or control, with subsequent crossover to the other condition. In the intervention arm, targeted dose was 75 minutes of robot-assisted walking, spread across three days/week. Implementation strategies for the child's classroom team included educational materials, centralized technical assistance, school champions, and on-going training. Implementation strategy evaluation focused on intervention protocol feasibility (i.e., weekly device use, troubleshooting) and fidelity (documentation of implementation strategy modifications). Preliminary estimates participation goal changes were obtained using the Canadian Occupational Performance Measure (COPM) 'Performance' scale (/10). Results: Eight children with neurodevelopmental physical disabilities participated (mean age 3.5, SD 0.5 years). Device use varied from 2.1 to 4.3 days/week (median 3.3) with a range of 48 to 96 minutes/week (median 80). Target use of >= 3 days/week was achieved for 66% of weeks and met the >=75-minute target for 33% of weeks. Troubleshooting occurred in 18.5% of sessions. Two implementation strategies were modified to improve feasibility. COPM mean change was 3.56 (SD 1.07) for robot-assisted walking and 2.11 (SD 2.41) for control (dz=0.74). Conclusions: This pilot study provided preliminary evidence regarding both the feasibility of robot-assisted walking implementation in schools and its potential effectiveness for children with physical disabilities. Findings support progression to a definitive trial.

Chronic intermittent hypobaric hypoxia is an occupational exposure experienced by workers who alternate between high-altitude worksites and lower-altitude resting locations. Its effects on cardiac autonomic modulation and electrocardiographic parameters remain insufficiently characterized in Andean workers. This study aimed to compare heart rate variability and electrocardiographic parameters between workers exposed to chronic intermittent hypobaric hypoxia and controls working permanently at lower altitude in Arequipa, Peru. An observational cross-sectional study was conducted in 96 male workers aged 20-60 years. The exposed group included 53 workers performing rotational shifts at altitudes [&ge;]3,000 m above sea level with rest periods at <2,500 m, while the control group included 43 workers working permanently at <2,500 m. Heart rate variability was assessed during the morning rest period in Arequipa, at approximately 2,300 25 m above sea level, using a Polar(R) H10 heart rate sensor after 15 minutes of rest. Participants also underwent standard 12-lead electrocardiography. Vital signs were clinically similar between groups. No statistically significant differences were observed in PR interval, corrected QT interval, time-domain heart rate variability parameters, or frequency-domain heart rate variability parameters. In this sample of apparently healthy male workers evaluated during their rest period at moderate altitude, exposure to chronic intermittent hypobaric hypoxia was not associated with significant differences in resting heart rate variability or standard electrocardiographic parameters.

Background: Conventional performance metrics do not indicate whether artificial intelligence (AI)-platforms and radiologists are right or wrong on the same examinations, nor whether their non-agreement is more often beneficial or harmful. Objective: To develop, illustrate, and apply directional metrics quantifying non-agreement between radiologists and AI-platforms against a common reference standard, and to adapt a fragility-based framework for quantifying the statistical robustness of these metrics. Methods: We defined Positive Non-Agreement (PNA, AI-platform correct, radiologist incorrect), Negative Non-Agreement (NNA, radiologist correct, AI-platform incorrect), and Net Non-Agreement (NENA, their difference). Metrics were illustrated in three hypothetical scenarios of 1,000 examinations each and applied to three previously published AI-radiologist studies on pulmonary embolism and mammography. Bootstrap confidence intervals were calculated, and a reclassification fragility index (FI) quantified the minimum case reclassification needed to remove statistical support for NENA's direction. Results: In illustration, NENA was positive in two scenarios and negative in one. In the three published studies, NENA ranged from -4.18% to +10.74% and decreased consistently with increasing radiologist experience (junior residents, +6.27%; consultants, +1.45%), a gradient driven primarily by declining PNA with modest increase in NNA. The reclassification FI was defined for all three illustrative scenarios (range, 0.10%-1.70%) and for six of the seven comparisons from these studies (range, 0.34%-4.31%), but not for the weakest comparison, whose confidence interval for NENA included zero. Conclusions: PNA, NNA, and NENA provide a directional, case-level complement to conventional performance metrics for AI-radiologist non-agreement. The accompanying reclassification FI offers a statistically grounded, generalizable method for quantifying their robustness.

Rural patients with heart failure (HF) have poor access to care, worse outcomes, and may benefit from at-home monitoring. We evaluated whether foot temperature measured using a thermographic floor mat was associated with invasively measured cardiac output and whether longitudinal temperature changes preceded HF events. We conducted a two-part prospective, observational single-center study using the SmartMat (Podimetrics, Somerville, MA), which acquires plantar thermograms and ambient temperature. In the acute study, patients undergoing clinically indicated right heart catheterization completed 4 scans on the same day. In the chronic study, patients with a HF hospitalization in the past year and New York Heart Association Class II-III symptoms were instructed to use the device and a scale daily for 6 months. A HF event was defined as receiving at least one dose of intravenous or subcutaneous diuretic. Thirty patients were enrolled in the acute study. Mean right hallux temperature positively correlated with thermodilution cardiac output (r=0.63, p<0.001). 42 patients were enrolled in the chronic study. Among patients experiencing an HF event, ambient-adjusted hallux temperature declined in the 15 days immediately preceding the event compared with days 15-30 before the event. Plantar thermograms are associated with invasively measured cardiac output and may detect subclinical worsening before HF events. Foot temperature may provide complementary information regarding HF decompensation, supporting further evaluation of plantar thermography for remote HF monitoring.

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.

The -Bonacci weighted sequence space {ell}(,) is introduced for [&ge;] 2, 1 [&le;] < {infty}, and > 0). It is proved to be a Banach space which is isometrically isomorphic to {ell}, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order - / are also established. Based on this framework, Scaled &#119898-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant &#119898-Bonacci root and employs depth wise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and a 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset while reducing parameters and FLOPs by 47.10% and 44.48%, respectively. On the CKD ultrasound dataset, Hexanacci-Net achieved 83.75% accuracy and a 0.7941 macro F1-score, with corresponding reductions of 50.37% and 45.10%. These findings support dominant-root scaling as a structured approach to lightweight CNN design.

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.

Background: Cardiovascular (CV) screening is critical for personnel deploying to Antarctica, where geographic isolation and limited medical care make the management of acute cardiovascular events challenging. The utility of contemporary risk prediction tools such as the AHA PREVENT equations in this unique population remains uncertain. Methods: We retrospectively analyzed 154 individuals referred for enhanced CV evaluation before deployment to the U.S. Antarctic Program (USAP). Participants were evaluated across three temporal cohorts (2018-2020, 2021-2023, and 2024-2026). Demographics, CV risk factors, cardiac testing, and deployment clearance outcomes were collected. Risk was estimated using the Framingham Risk Score (FRS), AHA/ACC Pooled Cohort Equations (PCE), and AHA PREVENT risk models. Results: The cohort was predominantly male (79.9%; mean age 44.6 {+/-} 14.6 years), and abnormal ECG was the most common referral trigger (70.8%). Median 10-year risk was low by PREVENT and FRS and borderline elevated by PCE (PREVENT 2.7% [IQR 1.1-6.6], FRS 2.3% [IQR 0.5-7.1], and PCE 6.1% [IQR 2.4-9.2]). Among 72 participants with all scores, 51% were classified discordantly; PREVENT ASCVD agreed closely with FRS (=0.77) but poorly with PCE (=0.24-0.35), which consistently estimated higher risk. PCE was calculable in 52.6% versus 70.8% for PREVENT. Overall, 132 participants (85.7%) were cleared for deployment, while 8 (5.2%) were not physically qualified (NPQ); 6 of these NPQs were due to cardiovascular causes. Of 22 cardiac medical evacuations during the study period, 21 involved individuals without prior advanced cardiovascular screening. One screened individual was evacuated for symptoms subsequently found to have no cardiac cause. Conclusions: Risk calculators disagree substantially in the fitness-selected population. We recommend PREVENT as the primary risk-stratification tool as it is the most contemporary derivation, incorporates renal function, is race-agnostic, extends to heart failure, applies from age 30, and agrees closely with FRS, easing transition. Targeted imaging, including coronary artery calcium scoring, may supplement intermediate-risk candidates.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Clinical records preserve patient findings and final recommendations but rarely capture how specialists select evidence, develop alternative interpretations, disagree, and converge on a management strategy. We describe a dataset derived from ten retrospectively selected, completed cases of histologically confirmed colorectal cancer. For each case, an administrator uploaded de-identified textual clinical records to an authenticated web-based annotation platform. A medical oncologist, surgical oncologist, and radiation oncologist, each with more than ten years of clinical experience, independently linked relevant source fragments to patient-specific interpretations and documented clinical problems, differential hypotheses, uncertainty, and diagnostic and treatment strategies. After the three records were locked, a large language model generated candidate questions for review by the tumour-board chair. The four physicians then discussed each case online; recordings were automatically transcribed and verified by two medical residents. The dataset comprises 30 source-linked specialist reasoning records, 10 verified case-level discussion transcripts, 10 final tumour-board decisions, and associated provenance and quality-control metadata. It supports research on evidence-grounded reasoning, cross-specialty disagreement, multidisciplinary decision-making, and multi-agent clinical decision support.

Background. Real-world 12-lead electrocardiograms (ECGs) often have missing or corrupted leads. A deep-learning classifier can be prepared for this by random lead masking during training, or by injecting clinical knowledge, for example by pulling its attention towards the leads that guidelines use for each diagnosis. Whether this knowledge adds robustness beyond masking is unknown. Methods. Pre-registered comparison of three arms sharing one network (per-lead 1D-ResNet encoder and per-diagnosis attention over the 12 leads): standard training (M0); random whole-lead masking (M1); masking plus a Kullback-Leibler penalty pulling attention towards a fixed guideline-derived key-lead map (M2, guideline-based attention guidance). Six diagnoses; five seeds per arm. Development on PTB-XL (21,799 ECGs); single sealed evaluation on PTB-XL fold 10 (2,198 ECGs) and on CODE-15% (233,204 exams, external validation). Primary endpoint: AUROC difference M2 - M1, averaged over first-degree AV block, right and left bundle branch block and atrial fibrillation and over 1 to 6 lost (zeroed) leads, with a patient-level paired BCa bootstrap 95% confidence interval. Results. The guidance did not change robustness: {Delta}AUROC -0.0010 (95% CI -0.0033 to +0.0006), an interval entirely within the pre-specified +-0.02 margin; the result was unchanged with another bootstrap seed, other interval methods and leave-one-diagnosis-out analyses. Masking improved AUROC: M1 - M0 = +0.0134 (95% CI +0.0102 to +0.0172) when one key lead was lost, and the number of lost leads tolerated before AUROC fell by more than 0.02 rose from 7 (M0) to 10 (M1). M2 attention matched the map and M2 was non-inferior on intact ECGs, but its calibration slope was lower for all six diagnoses in both datasets. M2 lost slightly more when all key leads were lost together (-0.0067) and gained only when one to three leads remained (exploratory; AUROC 0.9408 vs 0.9210 with 11 leads lost). CODE-15% reproduced every pattern (primary -0.0007, 95% CI -0.0011 to -0.0003). Conclusions. Random lead masking suffices to make an ECG classifier robust to missing leads; adding guideline knowledge aligns attention with clinical criteria but adds no robustness and worsens calibration. Guideline-aligned attention should not be taken as evidence of robustness.

Purpose: Posterior pharyngeal wall (PPW) thickening is a common structural manifestation of treatment-related internal lymphedema in patients with head and neck cancer and can be evaluated using lateral videofluoroscopic images acquired during modified barium swallow (MBS) studies. However, no standardized method currently exists for quantitative measurement. Methods: This two-phase methodological study first conducted a scoping review to characterize existing approaches to PPW thickness measurement and identify the principal factors contributing to variation in PPW thickness measurement. Eight studies were identified with variation observed across these measurement factors. An empirical reliability study was then performed using 107 MBS studies from 93 Head and Neck Cancer patients. Two independent raters annotated 10 anatomical landmarks on pre-swallow and post-swallow resting frames, and a computer-assisted algorithm quantitatively generated seven candidate PPW thickness measurements based on different vertebral reference points and measurement directions. Reliability was assessed intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was highest for the measurement originating from the midpoint of C3 and extending perpendicular to the anterior C3 surface (ICC, 0.78; 95% CI, 0.72-0.83). Among the normalization references, C2-C4 vertebral length demonstrated greater reliability than C3 vertebral length, with an ICC of 0.88 (95% CI, 0.85-0.91). No significant differences were observed between pre-swallow and post-swallow PPW thickness measurements across the evaluated approaches. Conclusions: These findings provide a methodological foundation for more standardized and reliable quantification of PPW thickness, with potential to expand the role of MBS from the functional evaluation of swallowing to include quantitative characterization of pharyngeal structural abnormalities.

Background Despite substantial improvements in maternal and child health over the past two decades, preventable maternal and neonatal morbidity and mortality remain major public health concerns in rural Bangladesh. Community-based interventions involving Community Volunteers (CVs), Community Groups (CGs), and Community Support Groups (CSGs) have been introduced to strengthen maternal and child health (MCH) services through health promotion, community engagement, and improved linkage with primary healthcare facilities. This study explored the roles, challenges, and opportunities of these community-based platforms in improving maternal and child health services. Methods A qualitative study was conducted in Kalaroa Upazila of Satkhira District, Bangladesh, between July and October 2020. Data were collected through focus group discussions and in-depth interviews with mothers, Community Volunteers, Community Group members, and Community Support Group members. Participants were purposively selected to capture diverse experiences from intervention and comparison areas. Interviews were conducted using semi-structured guides, audio-recorded with informed consent, transcribed verbatim, translated into English, and analyzed thematically using an inductive approach. Results Participants from intervention areas demonstrated greater knowledge of birth preparedness, antenatal care, pregnancy danger signs, institutional delivery, postnatal care, and newborn care than those from comparison areas. Community Volunteers, CGs, and CSGs played important roles in identifying pregnant women, conducting household counselling, promoting skilled maternal healthcare, facilitating referrals, and strengthening community participation. Participants perceived that these activities increased maternal awareness, improved health-seeking behaviour, and enhanced utilization of maternal and newborn health services. However, persistent barriers, including socio-cultural beliefs, financial constraints, transportation difficulties, shortages of skilled healthcare providers, and inadequate logistical support for community volunteers, continued to limit equitable access to quality services. Participants recommended strengthening community awareness programmes, improving community clinic readiness, enhancing referral systems, and providing regular training and supportive supervision for community-based health workers. Conclusions Community-based maternal health interventions involving Community Volunteers, Community Groups, and Community Support Groups contribute to improved maternal knowledge, health-seeking behaviour, and utilization of essential maternal and newborn health services in rural Bangladesh. Sustaining these gains will require stronger primary healthcare services, continued community engagement, and targeted strategies to address persistent health system and socio-cultural barriers. These findings provide important evidence for strengthening community-based maternal and child health programmes in Bangladesh and similar resource-constrained settings.

Community-led urban greening may contribute to neighborhood safety, but its relationship with crime remains uncertain. We examined whether Keep Indianapolis Beautiful Project GreenSpaces in Indianapolis were associated with changes in violence and total crime. We used a matched-pair difference-in-differences design comparing funded sites with matched unfunded applicant sites. The primary analysis included 29 matched pairs with two years of pre- and post-grant follow-up. Monthly Part I violent offenses and total crime within 600-m buffers were modeled using Poisson pseudo-maximum likelihood. In the first post-grant year, the violent-crime estimate was lower but imprecise (IRR, 0.903; 95% CI, 0.805-1.014). In the second post-grant year, the relative rate was lower (IRR, 0.830; 95% CI, 0.718-0.961). Total crime estimates showed no clear post-grant change. Additional analyses indicated some sensitivity to spatial scale, but no sustained divergence in pre-grant crime patterns. Analyses of short-term crime dependence showed clear month-to-month persistence in violent crime within sites but limited evidence of stable spatial dependence across nearby sites. Therefore, within the two-year post-grant period examined, Project GreenSpaces was associated most consistently with lower relative violent-crime rates in the second year, while no corresponding decrease was observed for total recorded crime. Longer follow-up is needed to determine whether these patterns persist or change as projects mature.