Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial: detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor.

Aims: Recent studies suggest that atrial fibrillation (AF) burden (time AF is present) is an independent risk factor for stroke. The aim of this trial was to study the feasibility and accuracy to identify AF episodes and quantify AF burden in patients with a known history of paroxysmal AF with a photoplethysmography (PPG)-based wearable. Methods and results: In this prospective, single-centre trial, the PPG-based estimation of AF burden was compared with measurements of a conventional 48 h Holter electrocardiogram (ECG), which served as the gold standard. An automated algorithm performed PPG analysis, while a cardiologist, blinded for the PPG data, analysed the ECG data. Detected episodes of AF measured by both methods were aligned timewise.Out of 100 patients recruited, 8 had to be excluded due to technical issues. Data from 92 patients were analysed [55.4% male; age 73.3 years (standard deviation, SD: 10.4)]. Twenty-five patients presented AF during the study period. The intraclass correlation coefficient of total AF burden minutes detected by the two measurement methods was 0.88. The percentage of correctly identified AF burden over all patients was 85.1% and the respective parameter for non-AF time was 99.9%. Conclusion: Our results demonstrate that a PPG-based wearable in combination with an analytical algorithm appears to be suitable for a semiquantitative estimation of AF burden in patients with a known history of paroxysmal AF. Trial Registration number: NCT04563572.

Radiological feature heterogeneity supports etiological diversity among patient groups in Meniere's disease.

We aimed to determine the prevalence of radiological temporal bone features that in previous studies showed only a weak or an inconsistent association with the clinical diagnosis of Meniere's disease (MD), in two groups of MD patients (n = 71) with previously established distinct endolymphatic sac pathologies; i.e. the group MD-dg (ES degeneration) and the group MD-hp (ES hypoplasia). Delayed gadolinium-enhanced MRI and high-resolution CT data were used to determine and compare between and within (affected vs. non-affected side) groups geometric temporal bone features (lengths, widths, contours), air cell tract volume, height of the jugular bulb, sigmoid sinus width, and MRI signal intensity alterations of the ES. Temporal bone features with significant intergroup differences were the retrolabyrinthine bone thickness (1.04 ± 0.69 mm, MD-hp; 3.1 ± 1.9 mm, MD-dg; p < 0.0001); posterior contour tortuosity (mean arch-to-chord ratio 1.019 ± 0.013, MD-hp; 1.096 ± 0.038, MD-dg; p < 0.0001); and the pneumatized volume (1.37 [0.86] cm3, MD-hp; 5.25 [3.45] cm3, MD-dg; p = 0.03). Features with differences between the affected and non-affected sides within the MD-dg group were the sigmoid sinus width (6.5 ± 1.7 mm, affected; 7.6 ± 2.1 mm, non-affected; p = 0.04) and the MRI signal intensity of the endolymphatic sac (median signal intensity, affected vs. unaffected side, 0.59 [IQR 0.31-0.89]). Radiological temporal bone features known to be only weakly or inconsistently associated with the clinical diagnosis MD, are highly prevalent in either of two MD patient groups. These results support the existence of diverse-developmental and degenerative-disease etiologies manifesting with distinct radiological temporal bone abnormalities.

Wearable Technologies for Pediatric Patients with Surgical Infections-More than Counting Steps?

Reliable vital sign assessments are crucial for the management of patients with infectious diseases. Wearable devices enable easy and comfortable continuous monitoring across settings, especially in pediatric patients, but information about their performance in acutely unwell children is scarce. Vital signs were continuously measured with a multi-sensor wearable device (Everion®, Biofourmis, Zurich, Switzerland) in 21 pediatric patients during their hospitalization for appendicitis, osteomyelitis, or septic arthritis to describe acceptance and feasibility and to compare validity and reliability with conventional measurements. Using a wearable device was highly accepted and feasible for health-care workers, parents, and children. There were substantial data gaps in continuous monitoring up to 24 h. The wearable device measured heart rate and oxygen saturation reliably (mean difference, 2.5 bpm and 0.4% SpO2) but underestimated body temperature by 1.7 °C. Data availability was suboptimal during the study period, but a good relationship was determined between wearable device and conventional measurements for heart rate and oxygen saturation. Acceptance and feasibility were high in all study groups. We recommend that wearable devices designed for medical use in children be validated in the targeted population to assure future high-quality continuous vital sign assessments in an easy and non-burdening way.

Shape Trumps Size: Image-Based Morphological Analysis Reveals That the 3D Shape Discriminates Intracranial Aneurysm Disease Status Better Than Aneurysm Size.

Background: To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status. Methods: 3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms. Results: The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies Z N s u r f (AUC = 0.80) and the modified writhe-index W ¯ m e a n L 1 (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases. Conclusions: Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms.

Shape irregularity of the intracranial aneurysm lumen exhibits diagnostic value.

BACKGROUND: Morphological irregularity is linked to intracranial aneurysm wall instability and manifests in the lumen shape. Yet there is currently no consent on how to assess shape irregularity. The aims of this work are to quantify irregularity as perceived by clinicians, to break down irregularity into morphological attributes, and to relate these to clinically relevant factors such as rupture status, aneurysm location, and patient age or sex. METHODS: Thirteen clinicians and 26 laypersons assessed 134 aneurysm lumen segmentations in terms of overall perceived irregularity and five different morphological attributes (presence/absence of a rough surface, blebs, lobules, asymmetry, complex geometry of the parent vasculature). We examined rater agreement and compared the ratings with clinical factors by means of regression analysis or binary classification. RESULTS: Using rank-based aggregation, the irregularity ratings of clinicians and laypersons did not differ statistically. Perceived irregularity showed good agreement with curvature (coefficient of determination R2 = 0.68 ± 0.08) and was modeled very accurately using the five morphological rating attributes plus shape elongation (R2 = 0.95 ± 0.02). In agreement with previous studies, irregularity was associated with aneurysm rupture status (AUC = 0.81 ± 0.08); adding aneurysm location as an explanatory variable increased the AUC to 0.87 ± 0.09. Besides irregularity, perceived asymmetry, presence of blebs or lobules, aneurysm size, non-sphericity, and curvature were linked to rupture. No association was found between morphology and any of patient sex, age, and history of smoking or hypertension. Aneurysm size was linked to morphology. CONCLUSIONS: Irregular lumen shape carries significant information on the aneurysm's disease status. Irregularity constitutes a continuous parameter that shows a strong association with the rupture status. To improve the objectivity of morphological assessment, we suggest examining shape through six different morphological attributes, which can characterize irregularity accurately.

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics.

OBJECTIVE: Incidental aneurysms pose a challenge for physicians, who need to weigh the rupture risk against the risks associated with treatment and its complications. A statistical model could potentially support such treatment decisions. A recently developed aneurysm rupture probability model performed well in the US data used for model training and in data from two European cohorts for external validation. Because Japanese and Finnish patients are known to have a higher aneurysm rupture risk, the authors' goals in the present study were to evaluate this model using data from Japanese and Finnish patients and to compare it with new models trained with Finnish and Japanese data. METHODS: Patient and image data on 2129 aneurysms in 1472 patients were used. Of these aneurysm cases, 1631 had been collected mainly from US hospitals, 249 from European (other than Finnish) hospitals, 147 from Japanese hospitals, and 102 from Finnish hospitals. Computational fluid dynamics simulations and shape analyses were conducted to quantitatively characterize each aneurysm's shape and hemodynamics. Next, the previously developed model's discrimination was evaluated using the Finnish and Japanese data in terms of the area under the receiver operating characteristic curve (AUC). Models with and without interaction terms between patient population and aneurysm characteristics were trained and evaluated including data from all four cohorts obtained by repeatedly randomly splitting the data into training and test data. RESULTS: The US model's AUC was reduced to 0.70 and 0.72, respectively, in the Finnish and Japanese data compared to 0.82 and 0.86 in the European and US data. When training the model with Japanese and Finnish data, the average AUC increased only slightly for the Finnish sample (to 0.76 ± 0.16) and Finnish and Japanese cases combined (from 0.74 to 0.75 ± 0.14) and decreased for the Japanese data (to 0.66 ± 0.33). In models including interaction terms, the AUC in the Finnish and Japanese data combined increased significantly to 0.83 ± 0.10. CONCLUSIONS: Developing an aneurysm rupture prediction model that applies to Japanese and Finnish aneurysms requires including data from these two cohorts for model training, as well as interaction terms between patient population and the other variables in the model. When including this information, the performance of such a model with Japanese and Finnish data is close to its performance with US or European data. These results suggest that population-specific differences determine how hemodynamics and shape associate with rupture risk in intracranial aneurysms.

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts.

BACKGROUND: For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions. METHODS: Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model's performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts. RESULTS: When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model's prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14). CONCLUSIONS: The model's performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.