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The objective of the study is to investigate the effect of Nuchal Fold (NF) in predicting Fetal Growth Restriction (FGR) using machine learning (ML), to explain the model's results using model-agnostic interpretable techniques, and to compare the results with clinical guidelines. This study used second-trimester ultrasound biometry and Doppler velocimetry were used to construct six FGR (birthweight < 3rd centile) ML models. Interpretability analysis was conducted using Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP). The results were compared with clinical guidelines based on the most optimal model. Support Vector Machine (SVM) exhibited the most consistent performance in FGR prediction. SHAP showed that the top contributors to identify FGR were Abdominal Circumference (AC), NF, Uterine RI (Ut RI), and Uterine PI (Ut PI). ALE showed that the cutoff values of Ut RI, Ut PI, and AC in differentiating FGR from normal were comparable with clinical guidelines (Errors between model and clinical; Ut RI: 15%, Ut PI: 8%, and AC: 11%). The cutoff value for NF to differentiate between healthy and FGR is 5.4 mm, where low NF may indicate FGR. The SVM model is the most stable in FGR prediction. ALE can be a potential tool to identify a cutoff value for novel parameters to differentiate between healthy and FGR.

Fetal growth restriction (FGR) affects 5–10% of pregnancies, leading to clinically significant fetal morbidity and mortality. FGR placentae frequently exhibit poor vascular branching, but the mechanisms driving this are poorly understood. We hypothesize that vascular structural malformation at the organ level alters microvascular shear stress, impairing angiogenesis. A computational model of placental vasculature predicted elevated placental micro-vascular shear stress in FGR placentae (0.2 Pa in severe FGR vs 0.05 Pa in normal placentae). Endothelial cells cultured under predicted FGR shear stresses migrated significantly slower and with greater persistence than in shear stresses predicted in normal placentae. These cell behaviors suggest a dominance of vessel elongation over branching. Taken together, these results suggest (1) poor vascular development increases vessel shear stress, (2) increased shear stress induces cell behaviors that impair capillary branching angiogenesis, and (3) impaired branching angiogenesis continues to drive elevated shear stress, jeopardizing further vascular formation. Inadequate vascular branching early in gestation could kick off this cyclic loop and continue to negatively impact placental angiogenesis throughout gestation.

In cross-subject electroencephalography (EEG) motor imagery decoding tasks, significant physiological differences among individuals pose substantial challenges. Although Gaussian-based softmax Deep Domain Adaptation (DDA) methods have achieved considerable progress, they remain highly dependent on target domain data, which is inconsistent with real-world scenarios where target domain data may be inaccessible or extremely limited. Moreover, existing DDA methods primarily achieve feature alignment by minimizing distribution discrepancies between the source and target domains. However, given the pronounced physiological variability across individuals, simple distribution matching strategies often fail to effectively mitigate domain shift, thereby limiting generalization performance. To address these challenges, this study proposes an improved Gaussian-based softmax Deep Domain Generalization (Exp-G-softmax DDG) framework, which aims to overcome the limitations of traditional DDG methods in handling inter-class differences and cross-domain distribution shifts. By introducing multi-source domain joint training and an enhanced G-softmax function, the proposed method effectively resolves the dynamic balance between intra-class distance and inter-class distance. The Exp-G-softmax DDG mechanism integrates class center information, thereby enhancing model robustness and improving its ability to learn discriminative feature representations, ultimately leading to superior classification performance. Experimental results demonstrate that the proposed method achieves classification performance comparable to that of DDA on three publicly available real-world EEG datasets, providing a novel solution for cross-subject motor imagery decoding. The source code is available at: https://github.com/dawin2015/G-softmax-DDG.

Intrauterine growth restriction (IUGR) is a pregnancy complication due to placental dysfunction that prevents the fetus from obtaining enough oxygen and nutrients, leading to serious mortality and morbidity risks. There is no treatment for IUGR despite having a prevalence of 3% in developed countries, giving rise to an urgency to improve our understanding of the disease. Applying biomechanics investigation on IUGR placental tissues can give important new insights. We performed pressure-diameter mechanical testing of placental chorionic arteries and found that in severe IUGR cases (RI > 90 th centile) but not in IUGR cases (RI < 90 th centile), vascular distensibility was significantly increased from normal. Constitutive modeling demonstrated that a simplified Fung-type hyperelastic model was able to describe the mechanical properties well, and histology showed that severe IUGR had the lowest collagen to elastin ratio. To demonstrate that the increased distensibility in the severe IUGR group was related to their elevated umbilical resistance and pulsatility indices, we modelled the placental circulation using a Windkessel model, and demonstrated that vascular compliance (and not just vascular resistance) directly affected blood flow pulsatility, suggesting that it is an important parameter for the disease. Our study showed that biomechanics study on placenta could extend our understanding on placenta physiology.

Intrauterine Growth Restriction (IUGR) is a serious and prevalent pregnancy complication that is due to placental insufficiency and IUGR babies suffer significantly higher risks of mortality and morbidity. Current detection rate for IUGR is generally poor and thus an alternative diagnostic tool is needed to improve the IUGR detection. Elastography, a non-invasive method that measures the tissue stiffness, has been proposed as one such technique. However, to date, we have limited information on the mechanical properties of IUGR placenta. In this study, we investigated the mechanical properties of normal and IUGR placentae and prescribed a suitable hyperelastic model to describe their mechanical behaviors. A total of 46 normal and 43 IUGR placenta samples were investigated. Results showed that placenta samples were isotropic, but had a high spatial variability of stiffness. The samples also had significant viscoelasticity. IUGR placenta was observed to be slightly stiffer than normal placenta but the difference was significant only at compression rate of 0.25 Hz and with 20% compression depth. Three simple hyperelastic models—Yeoh, Ogden and Fung models, were found to be able to fit the experimentally measured mechanical behaviors, and Fung model performed slightly better. These results may be useful for optimizing placenta elastography for the detection of IUGR.

Significant reductions in blood flow and umbilical diameters were reported in pregnancies affected by intrauterine growth restriction (IUGR) from placental insufficiency. However, it is not known if IUGR umbilical blood vessels experience different hemodynamic wall shear stresses (WSS) compared to normal umbilical vessels. As WSS is known to influence vasoactivity and vascular growth and remodeling, which can regulate flow rates, it is important to study this parameter. In this study, we aim to characterize umbilical vascular WSS environment in normal and IUGR pregnancies, and evaluate correlation between WSS and vascular diameter, and gestational age. Twenty-two normal and 21 IUGR pregnancies were assessed via ultrasound between the 27th and 39th gestational week. IUGR was defined as estimated fetal weight and/or abdominal circumference below the 10th centile, with no improvement during the remainder of the pregnancy. Vascular diameter was determined by 3D ultrasound scans and image segmentation. Umbilical artery (UA) WSS was computed via computational flow simulations, while umbilical vein (UV) WSS was computed via the Poiseuille equation. Univariate multiple regression analysis was used to test for the differences between normal and IUGR cohort. UV volumetric flow rate, UA and UV diameters were significantly lower in IUGR fetuses, but flow velocities and WSS trends in UA and UV were very similar between normal and IUGR groups. In both groups, UV WSS showed a significant negative correlation with diameter, but UA WSS had no correlation with diameter, suggesting a constancy of WSS environment and the existence of WSS homeostasis in UA, but not in UV. Despite having reduced flow rate and vascular sizes, IUGR UAs had hemodynamic mechanical stress environments and trends that were similar to those in normal pregnancies. This suggested that endothelial dysfunction or abnormal mechanosensing was unlikely to be the cause of small vessels in IUGR umbilical cords.

The rising incidence of oral cancer and associated poor prognosis, primarily due to delayed diagnosis, highlight the urgent need for artificial intelligence tools in clinical detection. However, efforts in this regard are hampered by the lack of large and ethnically heterogenous image datasets of oral lesions with clinically validated diagnoses. To address this gap, oral mucosa images captured with mobile device cameras were collected from cohorts spanning five countries. The images were systematically annotated with lesion type classifications as well as specific clinical diagnoses, then assessed for quality. The diagnoses were verified retrospectively by biopsy, where applicable, or by consensus verification by dental experts. The final dataset consists of 30,039 oral mucosa images supplemented by clinical metadata, made available on the MeMoSA Workbench platform. We believe that the MeMoSA dataset will serve as a significant resource to drive the training, evaluation, and refinement of AI-driven diagnostic algorithms, potentially improving diagnostic accuracy and enabling rigorous benchmarking against clinical expert assessments, for the early detection of oral cancer.

[...]we clarify the definition of \"strain\". [...]from the user manual of the GE Logiq E9 ultrasound machine (Healthcare Inc., 2013), which was the machine used by Cimsit et al. we found that the \"strain ratio\" values (E2/E1) measured by Cimsit et al. were in fact the ratio of the elasticity indices, not of strain, and require some clarifications.

The endothelial cells of the umbilical vessels are frequently used in mechanobiology experiments. They are known to respond to wall shear stress (WSS) of blood flow, which influences vascular growth and remodeling. The in vivo environment of umbilical vascular WSS, however, is not well characterized. In this study, we performed detailed characterization of the umbilical vascular WSS environments using clinical ultrasound scans combined with computational simulations. Doppler ultrasound scans of 28 normal human fetuses from 32nd to 33rd gestational weeks were investigated. Vascular cross-sectional areas were quantified through 3D reconstruction of the vascular geometry from 3D B-mode ultrasound images, and flow velocities were quantified through pulse wave Doppler. WSS in umbilical vein was computed with Poiseuille’s equation, whereas WSS in umbilical artery was obtained via computational fluid dynamics simulations of the helical arterial geometry. Results showed that blood flow velocity for umbilical artery and vein did not correlate with vascular sizes, suggesting that velocity had a very weak trend with or remained constant over vascular sizes. Average WSS for umbilical arteries and vein was 2.81 and 0.52 Pa, respectively. Umbilical vein WSS showed a significant negative correlation with the vessel diameter, but umbilical artery did not show any correlation. We hypothesize that this may be due to differential regulation of vascular sizes based on WSS sensing. Due to the helical geometry of umbilical arteries, bending of the umbilical cord did not significantly alter the vascular resistance or WSS, unlike that in the umbilical veins. We hypothesize that the helical shape of umbilical arteries may be an adaptation feature to render a higher constancy of WSS and flow in the arteries despite umbilical cord bending.

BackgroundDengue is a significant global health threat, with nearly half of the world’s population at risk. Approximately 75% of the global dengue burden is concentrated in the Southeast Asia and Western Pacific regions. While most symptomatic dengue cases present as acute febrile illnesses, 5–20% progress to severe dengue, which is associated with considerable morbidity and mortality.The critical phase of dengue typically develops 3–7 days after the onset of symptoms and lasts 24–48 hours. During this time, a subset of patients may experience sudden and severe deterioration, making timely identification and management crucial. However, the complex and overlapping clinical profiles of dengue patients, coupled with the multifaceted nature of the disease, make it challenging for clinicians to accurately predict which patients will develop severe dengue. As a result, clinicians often adopt a cautious approach, assuming that all hospitalized patients are at risk of progressing to severe dengue. This leads to intensive monitoring of all patients, contributing to increased workloads and resource strain in hospital settings.ObjectiveThis study aimed to develop and validate a machine learning-based risk scoring system, the Dengue Severity Prognostication (DeSProg) system, to predict the progression of dengue patients to severe dengue (SD) in clinical settings. The system was designed to reduce overdiagnosis and unnecessary interventions while ensuring timely management of high-risk cases.MethodsThe project was conducted in two phases at a tertiary hospital in Malaysia. Phase I: Development of the DeSProg system, comprising a dengue e-clerking form and an SD prediction model. Data from dengue patients were analyzed using logistic regression, random forest, and one-class support vector machine (SVM) models. These models were trained on selected clinical features to determine the optimal prediction model. DeSProg was built using Python libraries, with SQL queries extracting and visualizing dengue-related data. The prediction model with the highest balanced accuracy was integrated into the system’s dashboard. Phase II: Piloting the DeSProg system in clinical settings. New dengue cases admitted between November 2021 and November 2022 were included. System-predicted outcomes were compared to clinicians’ diagnoses to assess its performance.ResultsThe logistic regression model achieved the highest balanced accuracy (80.15%) using 10 clinical features. In the pilot phase, the DeSProg system demonstrated an overall accuracy of 90.74%, with high specificity (93.08%) and a negative predictive value (NPV) of 97.19%. These results highlight its strong performance in identifying patients unlikely to progress to SD, thereby minimizing unnecessary interventions. However, the system’s sensitivity was 30.00%, with a positive predictive value (PPV) of 14.29%, indicating room for improvement in detecting patients at higher risk.ConclusionThe DeSProg system showcases the potential of artificial intelligence in mitigating overdiagnosis in dengue management. By accurately identifying low-risk patients, the system can prevent unnecessary treatments and hospitalizations, optimizing healthcare resources. However, the limited sensitivity underscores the need for further refinement to enhance its capability to identify high-risk cases. This study emphasizes the role of AI-powered tools in striking a balance between timely intervention and the avoidance of overdiagnosis in infectious disease management.