Self-Supervised Traversability Learning With Online Prototype Adaptation for Off-Road Autonomous Driving

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency. For high speed driving scenarios, latency is a crucial parameter as it provides more time to react to dangerous situations. Typically a voxel or point-cloud based 3D convolution approach is utilized for this module. Firstly, they are inefficient on embedded platforms as they are not suitable for efficient parallelization. Secondly, they have a variable runtime due to level of sparsity of the scene which is against the determinism needed in a safety system. In this work, we aim to develop a very low latency algorithm with fixed runtime. We propose a novel semantic segmentation architecture as a single unified model for object center detection using key points, box predictions and orientation prediction using binned classification in a simpler Bird's Eye View (BEV) 2D representation. The proposed architecture can be trivially extended to include semantic segmentation classes like road without any additional computation. The proposed model has a latency of 4 ms on the embedded Nvidia Xavier platform. The model is 5X faster than other top accuracy models with a minimal accuracy degradation of 2% in Average Precision at IoU=0.5 on KITTI dataset.

End-to-end autonomous driving has become a key research focus in autonomous vehicles. However, existing methods struggle with effectively fusing heterogeneous sensor inputs and converting dense perceptual features into sparse motion representations. To address these challenges, we propose BevDrive, a novel end-to-end autonomous driving framework that unifies camera–LiDAR fusion and motion planning through a bird’s-eye view (BEV) representation. BevDrive consists of three core modules: the bidirectionally guided BEV feature construction module, the dual-attention BEV feature fusion module, and the BEV-based motion planning module. The bidirectionally guided BEV feature construction module comprises two branches: depth-guided image BEV feature construction and image-guided LiDAR BEV feature construction. Depth-guided image BEV feature construction employs a lifting and projection approach guided by depth information from LiDAR, transforming image features into a BEV representation. Meanwhile, image-guided LiDAR BEV feature construction enriches sparse LiDAR BEV features by integrating complementary information from the images. Then, the dual-attention BEV feature fusion module combines multi-modal BEV features at both local and global levels using a hybrid approach of window self-attention and global self-attention mechanisms. Finally, the BEV-based motion planning module integrates perception and planning by refining control and trajectory queries through interactions with the scene context in the fused BEV features, generating precise trajectory points and control commands. Extensive experiments on the CARLA Town05 Long benchmark demonstrate that BevDrive achieves state-of-the-art performance. Furthermore, we validate the feasibility of the proposed algorithm on a real-world vehicle platform, confirming its practical applicability and robustness.

Background and AimsThe 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated interpretation often lack generalizability, remain closed source, and are primarily trained using supervised learning (SL), which requires extensive labelled datasets and may limit adaptability across diverse clinical settings. Self-supervised learning (SSL) can potentially overcome these limitations by learning robust representations from unlabelled data. To address these challenges, this study developed and compared two open-source foundational ECG models: DeepECG-SL, a supervised multilabel ECG model, and DeepECG-SSL, a self-supervised model.MethodsBoth models were trained on over 1 million ECGs using a standardized preprocessing pipeline and automated free-text extraction from ECG reports to predict 77 cardiac conditions. DeepECG-SSL leveraged unlabelled data through self-supervised contrastive learning and masked lead modelling before fine-tuning for downstream tasks, while DeepECG-SL was trained directly on labelled diagnostic data in an end-to-end fashion. Performance was evaluated across seven private, multilingual healthcare systems and four public ECG repositories, with assessment of fairness by age and sex, and investigation of privacy vulnerabilities as well as memory and compute requirements.ResultsDeepECG-SSL achieved micro-averaged area under the receiver operating characteristic curves (AUROCs) across all 77 cardiac conditions for ECG interpretation of 0.990 [95% confidence interval (CI): 0.990, 0.990] on the internal dataset (MHI-ds), 0.981 (95% CI: 0.981, 0.981) on external public datasets (UKB, CLSA, MIMIC-IV and PTB), and 0.983 (95% CI: 0.983, 0.983) on external private datasets (UW, UCSF, JGH, NYP, MGH, CSH and CHUM), while DeepECG-SL demonstrated AUROCs of 0.992 (95% CI: 0.992, 0.992), 0.980 (95% CI: 0.980, 0.980), and 0.983 (95% CI: 0.983, 0.984), respectively. Fairness analyses revealed minimal disparities (true-positive rate and false-positive rate difference <0.1) across age and sex groups for both models. DeepECG-SSL demonstrated superior performance on limited-data digital biomarker tasks, with the largest improvements in long QT syndrome (LQTS) genotype classification (AUROC 0.931 vs 0.850, P = .026, n = 127 ECGs) and 5 year atrial fibrillation risk prediction (AUROC 0.742 vs 0.734, P < 0.001, n = 132 050 ECGs), while achieving superior performance in left ventricular ejection fraction ≤40% classification (AUROC 0.926 vs 0.917, P < 0.001, n = 25 252 ECGs) and comparable performance in LQTS detection (AUROC 0.767 vs 0.735, P = 0.117, n = 934 ECGs).ConclusionsThis study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics. By releasing model weights, preprocessing tools, and validation code, this work aims to support robust, data-efficient AI diagnostics across diverse clinical environments and questions.

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

BackgroundFibroScan® Expert 630 is a novel fibroscan device equipped with SmartExam software, which allows continuous measurement of controlled attenuation parameter (CAP) during the entire examination and automatic measurement depths adaption...

Background: Sepsis continues to impose a major healthcare burden globally. Early identification and prompt initiation of appropriate therapy, particularly antibiotics, are critical determinants of survival. Various bedside scoring systems have been developed for early detection and prognostication of sepsis. Objective: This chapter compares the predictive accuracy of Systemic Inflammatory Response Syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), and Modified Early Warning Score (MEWS) for in-hospital mortality among patients with suspected sepsis admitted to the intensive care unit (ICU). Methods: This prospective comparative study was conducted in the Department of General Medicine and Critical Care at Dhiraj General Hospital, affiliated with Sumandeep Vidyapeeth University, Waghodia, Gujarat, India. A total of 188 adult patients with suspected sepsis admitted to the ICU were included. Clinical and laboratory parameters required to calculate SIRS, qSOFA, NEWS, and MEWS were recorded at admission. The Modified EWS (MEWS) score comprises five physiologic variables: systolic blood pressure (SBP), HR, RR, temperature, and mental status. The MEWS score considers the relative deviation from a patient’s normal blood pressure and urine output to identify surgical patients who would potentially benefit from intensive care. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC) were calculated for each scoring system in predicting ICU mortality. Hierarchical Summary Receiver Operating Characteristic (HSROC) curve analysis was performed to compare overall prognostic accuracy. Results: Among 188 patients, 42 (22.34%) died during hospitalisation. qSOFA ≥2 demonstrated the highest specificity (73.61%) but the lowest sensitivity (36.02%). SIRS ≥2 and NEWS ≥5 showed the highest sensitivity (77.78%), although SIRS had the lowest specificity (23.88%). MEWS ≥5 demonstrated balanced performance with a sensitivity of 76.36% and specificity of 63.91%. The highest discriminatory ability for ICU mortality was observed with MEWS ≥5 (AUROC 0.76; 95% CI 0.68–0.84), compared to NEWS ≥5 (AUROC 0.61), qSOFA ≥2 (AUROC 0.56), and SIRS ≥2 (AUROC 0.49). HSROC analysis confirmed the superior overall prognostic performance of MEWS. The better predictive accuracy of NEWS is most likely due to the fact that it incorporates a larger number of physiological parameters than qSOFA and SIRS, including the majority of their constituent atoms. Any scoring system for sepsis should favour better sensitivity over specificity because the consequences of false-negative results (delayed or missed therapy) are arguably much higher than those of false-positive results (unnecessary antibiotics). The AUROC for SIRS, however, was so low that it appears to have very little clinical use. Conclusion: Among the four scoring systems evaluated, MEWS demonstrated the highest predictive accuracy for ICU mortality. However, none of the scoring systems achieved both high sensitivity and high specificity. These tools should therefore complement, rather than replace, comprehensive clinical assessment.

This paper describes new vision-based registration methods utilizing not only cameras on a user's head-mounted display but also a bird's-eye view camera that observes the user from an objective viewpoint. Two new methods, the line constraint method (LCM) and global error minimization method (GEM), are proposed. The former method reduces the number of unknown parameters concerning the user's viewpoint by restricting it to be on the line of sight from the bird's-eye view. The other method minimizes the sum of errors, which is the sum of the distance between the fiducials on the view and the calculated positions of them based on the current viewing parameters, for both the user's view and the bird's-eye view. The methods proposed here reduce the number of points that should be observed from the user's viewpoint for registration, thus improving the stability. In addition to theoretical discussions, this paper demonstrates the effectiveness of our methods by experiments in comparison with methods that use only a user's view camera or a bird's-eye view camera.

Актуальность. Новые возможности улучшения функции внешнего дыхания (ФВД) связаны с применением высокочастотной осцилляции грудной клетки (ВЧОГК), которая позволяет восстановить дренаж бронхиального дерева и оптимизировать легочную вентиляцию. Цель исследования — изучение и оценка ФВД у детей с внебольничной пневмонией (ВП). Материалы и методы. Проведено обследование 107 детей (основная группа (ОГ) — 55 человек и контрольная группа (КГ) — 52 человека) в возрасте 6–17 лет с ВП с острым и неосложненным течением. Дети ОГ получали базисную терапию (БТ) с проведением процедур ВЧОГК, тогда как дети КГ получали исключительно БТ. Исследовали ФВД в динамике лечения, применяя спирометрию. Результаты. В начале терапии ВП показатели ФВД в исследуемых группах имели несущественные различия. При анализе параметров ФВД у детей ОГ на 10-й день БТ отмечено повышение объема форсированного выдоха за 1-ю секунду (ОФВ1) (88,36 ± 1,55 %, p = 0,02), жизненной емкости легких (ЖЕЛ) (88,18 ± 1,53 %, p = 0,02), форсированной жизненной емкости легких (ФЖЕЛ) (86,77 ± 1,37 %, p = 0,03), максимальной объемной скорости воздуха на уровне выдоха 25 % (МОШ25) (90,10 ± 2,99 %, p = 0,02) и максимальной вентиляции легких (МВЛ) (88,31 ± 1,70 %, p = 0,04). Сравнивая показатели ФВД детей КГ, выявили менее выраженную динамику, в частности, ОФВ1 (81,65 ± 2,44 %), ЖЕЛ (82,95 ± 2,56), ФЖЕЛ (80,85 ± 2,09), МОШ25 (82,63 ± 3,08 %) и МВЛ (85,65 ± 1,99 %). При проведении ROC-анализа динамика восстановления ФВД у детей ОГ более заметна за счет улучшения МВЛ, о чем свидетельствует самая большая площадь под кривой ROC (ППК) — 0,99, ОФВ1 — ППК 0,94 и ОФВ1/ФЖЕЛ — ППК 0,94. У детей КГ была менее выражена динамика исследуемых параметров ФВД, что подтверждается МВЛ — ППК 0,63, ОФВ1 — ППК 0,79 и ОФВ1/ФЖЕЛ — ППК 0,89. Выводы. Для улучшения вентиляционной функции легких у детей с ВП необходимо проводить процедуры бронходренажа, используя ВЧОГК в составе БТ, о чем свидетельствуют полученные положительные результаты спирометрии.

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. To explore how well the tri-compartmental RSIperforms in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. Prospective. 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion , hindered diffusion , and free diffusion ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. The , SUVmax were significantly higher, and , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( ) > AUC ( ) > AUC (ADC) > AUC ( ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. RSI-derived parameters ( , ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. Stage 2.

BackgroundThermal imaging (TI) is a portable, low cost imaging tool with high feasibility for use. Clinical joint assessment.Is routinely performed in rheumatoid arthritis (RA) patient care.ObjectivesTo assess a combined TI...

Circulating miRNAs previously associated with androgen excess in women might be used as diagnostic biomarkers for polycystic ovary syndrome (PCOS). Models based on circulating miR-142-3p and miR-598-3p expression show good discrimination among women with and without PCOS, particularly when coupled with easily available measurements such as waist-to-hip ratio (WHR) and circulating LH-to-FSH (LH/FSH) ratios. The lack of standardization of the signs, methods, and threshold values used to establish the presence of the diagnostic criteria (hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology) complicates the diagnosis of PCOS. Certain biomarkers may help with such a diagnosis. We conducted a validation study to check the diagnostic accuracy for PCOS of several miRNAs that were associated with the syndrome in a small pilot study that had been previously carried out by our research group. This was a diagnostic test study involving 140 premenopausal women. We included 71 women with PCOS and 69 healthy control women in the study. Both groups were selected as to be similar in terms of body mass index. We used miRCURY LNA™ Universal RT microRNA PCR to analyse the five miRNAs that had shown the strongest associations with PCOS in a much smaller pilot study previously conducted by our group. We studied diagnostic accuracy using receiver operating characteristics (ROC) curve analysis. Only the expression of two miRNAs, miR-142-3p and miR-598-3p, of the five studied, was different between the women with PCOS and the non-hyperandrogenic controls. The diagnostic accuracy of the combination of these circulating miRNAs was good (area under the ROC curve (AUC) 0.801; 95% CI: 0.72-0.88) and was further improved when adding WHR (AUC 0.834, 95% CI: 0.756-0.912), LH/FSH ratio (AUC = 0.869, 95% CI: 0.804-0.934) or both (AUC = 0.895, 95% CI: 0.835-0.954). We developed several models by selecting different threshold values for these variables favouring either sensitivity or specificity, with positive and negative predictive values as high as 88% or 85%, respectively. Patients included here had the classic PCOS phenotype, consisting of hyperandrogenism and ovulatory dysfunction; hence, the present results might not apply to milder phenotypes lacking androgen excess. If confirmed in larger studies addressing different populations and PCOS phenotypes, these biomarkers may be useful to simplify the clinical diagnosis of this prevalent syndrome. This research was funded by Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (grants PI15/01686, PIE16/00050, PI18/01122 & PI21/00116) and co-funded by European Regional Development Fund 'A way to make Europe'. Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) and Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) are also initiatives of the Instituto de Salud Carlos III. The authors have no competing interests to disclose. N/A.

The Medieval Church of St. Andrews. Edited by David McRoberts. Pp. xii + 178 + 24 illustrations + plan and a facsimile of the 16th-century ‘bird's eye view’ of St. Andrews. Glasgow: Burns, 1976. £7. - Volume 29 Issue 3

To determine the diagnostic value of preoperative inflammatory biomarkers and CA199, alone or in combination, in diagnosing pancreatic cancer (PCC). This retrospective study was comprised of 75 PCC patients and 83 healthy controls (HC). The participant's medical data was mined from the electronic records of the First Affiliated Hospital of Guangxi Medical University. The data included the preoperative circulating albumin/fibrinogen ratio (AFR), the platelet/lymphocyte ratio (PLR), the lymphocyte/monocyte ratio (LMR), the neutrophil/lymphocyte ratio (NLR), and the derived NLR (dNLR). The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUROC) were used to evaluate the diagnostic efficacy of these candidate biomarkers for PCC. A single AFR significantly distinguished PCC from the healthy controls (AUROC: 0.903, 95% CI: 0.846 - 0.945) and had a significantly higher sensitivity and larger AUROC than CA199 (AUROC: 0.814, 95% CI: 0.774 - 0.871). The combinations of AFR with CA199 (AUROC: 0.932, 95% CI: 0.881 - 0.966), RDW with CA199 (AUROC: 0.905, 95% CI: 0.849 - 0.946), Alb with CA199 (AUROC: 0.869, 95% CI: 0.806 - 0.917), and Fib with CA199 (AUROC: 0.921, 95% CI: 0.868 - 0.958) also yielded higher sensitivities and larger AUROCs than CA199 alone. Circulating AFR was an effective biomarker in diagnosing PCC. Combining AFR, RDW, Alb, and Fib with CA199 could improve the diagnostic efficacy for PCC.

Objective To explore the value of quantitative T2 weighted magnetic resonance imaging (T2WI) and diffusion tensor imaging (DTI) parameters for predicting functional outcomes after surgery for cervical spondylotic myelopathy (CSM). Methods One hundred and forty CSM patients received T2WI and DTI before surgery at Tianjin Hospital between April 2014 and April 2016. They were then given systematic rehabilitation treatment after the surgery. The Japanese Orthopaedics Association (JOA) scoring system was applied to evaluate their neurological function before and after the surgery. According to the JOA recovery rate at 1-year follow-up, the patients were divided into a good recovery group (recovery rate≥50%) and a poor recovery group (recovery rate <50%). Maximum spinal cord compression (MSCC), signal change ratio (SCR), transverse area (TA), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were compared between the two groups. Receiver operating characteristics (ROC) curves were used to measure the prognostic ability and determine the best cut-off value for each variable. The independent predictors of a poor recovery were estimated using univariate and multivariate analysis. Results ROC analysis showed that the area under the ROC curve (AUC) of MSCC was 0.593. For SCR it was 0.682. For the TA it was 0.706. For the ADC it was 0.719 and for FA it was 0.749. The respective cut-off values were 44% compression, 1.561, 0.46 cm2, 1.339×10-3 mm2/s and 0.386. FA had the largest AUC, followed by ADC, TA, SCR and MSCC. MSCC and SCR had low discrimination power (AUC<0.7) in predicting a poor recovery, whereas TA, ADC and FA had moderate discrimination power (AUC 0.7-0.9). Logistic multivariate regression showed that a low JOA score, TA≤0.46 cm2 or FA≤0.386 were independent risk factors for a poor recovery. A predicting model built according to the results of the logistic regression analysis gave an AUC of 0.87, significantly better than that of the JOA score. With a cut-off value of 0.36, the sensitivity and specificity were 80% and 77% respectively. Conclusions Combining T2WI and DTI parameters with the JOA score may better predict the recovery of patients with CSM. The values can also provide references for making up rehabilitation plans. Key words: T2 weighted magnetic resonance imaging; Diffusion tensor imaging; Cervical vertebrae; Spinal cord compression; Prognosis

Objectives: Herpes virus entry mediator (HVEM) is a costimulatory molecule, and has been proved to play an important role in airway inflammatory and remodeling processes of asthma. We aimed to investigate the expression of HVEM gene in patients with asthma as a means of assessing disease severity.Methods: This study was carried out on 59 subjects, 16 patients with mild persistent asthma, 11 patients with moderate persistent asthma, 13 patients with severe persistent asthma, and 19 age and gender matched healthy controls. The HVEM mRNA expressions of all subjects were determined by real time PCR. Correlations between HVEM mRNA expression and fractional exhaled nitric oxide (FeNO), pulmonary function test values, total blood white cell count and differential, total immunoglobulin E (IgE) level, and Asthma Control Test (ACT) score were analyzed, respectively. The discrimination abilities of HVEM mRNA between different groups were tested using receiver operating characteristics (ROC) curve analyses.Results: This study showed the expressions of HVEM mRNA were significantly higher in the patients with severe and moderate persistent asthma than in patients with mild persistent asthma and healthy subjects (2.97 ± 1.23 vs. 1.17 ± 0.42 vs. 0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p < 0.001), but there was no significant difference between patients with mild persistent asthma and health controls (0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p = 0.557). HVEM mRNA expression at cut off point [1.01/NAPDH, area under the ROC curve (AUC) = 0.99] is sufficient to discriminate severe patients from mild-to-moderate patients, and at cut off point (0.93/NAPDH, AUC = 0.91) for discrimination of moderate-to-severe patients from mild ones, while at cut off point (0.76/NAPDH, AUC = 0.75) for discrimination of asthmatic patients from controls. Furthermore, HVEM mRNA expression was positively correlated with FeNO level (r = 0.524, p = 0.015), and total lymphocyte count (r = 0.426, p = 0.017) in patients with persistent asthma.Conclusions: HVEM gene expressions can be used as a potential biomarker for evaluating the severity of patients with persistent asthma.