Bystander defibrillation for out-of-hospital cardiac arrest in Ireland.

Aims: To describe and explore predictors of bystander defibrillation in Ireland during the period 2012 to 2020. To examine the relationship between bystander defibrillation and health system developments. Methods: National level Out of Hospital Cardiac Arrest (OHCA) registry data were interrogated, focusing on patients who had defibrillation performed. Bystander defibrillation (as compared to EMS initiated defibrillation) was the key outcome of concern. Logistic regression models were built and refined by fitting predictors, performing stepwise variable selection and by adding pairwise interactions that improved fit. Results: The data included 5,751 cases of OHCA where defibrillation was performed. Increasing year over time (OR 1.17, 95% CI 1.13, 1.21) was associated with increased adjusted odds of bystander defibrillation. Non-cardiac aetiology was associated with reduced adjusted odds of bystander defibrillation (OR 0.30, 95% CI 0.21, 0.42), as were increasing age in years (OR 0.99, 95% CI 0.987, 0.996) and night-time occurrence of OHCA (OR 0.67, 95% CI 0.53, 0.83). Six further variables in the final model (sex, call response interval, incident location (home or other), who witnessed collapse (bystander or not witnessed), urban or rural location, and the COVID period) were involved in significant interactions. Bystander defibrillation was in general less likely in urban settings and at home locations. Whilst women were less likely to receive bystander defibrillation overall, in witnessed OHCAs, occurring outside the home, in urban areas and outside of the COVID-19 period women were more likely, to receive bystander defibrillation. Conclusions: Defibrillation by bystanders has increased incrementally over time in Ireland. Interventions to address sex and age-based disparities, alongside interventions to increase bystander defibrillation at night, in urban settings and at home locations are required.

Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL.

BACKGROUND: Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging. METHOD: We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition. RESULTS: The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression. CONCLUSION: Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.

Automatic segmentation and classification of Papanicolaou-stained cells and dataset for oral cancer detection.

BACKGROUND AND OBJECTIVE: Papanicolaou staining has been successfully used to assist early detection of cervix cancer for several decades. We postulate that this staining technique can also be used for assisting early detection of oral cancer, which is responsible for about 300,000 deaths every year. The rational for such claim includes two key observations: (i) nuclear atypia, i.e., changes in volume, shape, and staining properties of the cell nuclei can be linked to rapid cell proliferation and genetic instability; and (ii) Papanicolaou staining allows one to reliably segment cells' nuclei and cytoplasms. While Papanicolaou staining is an attractive tool due to its low cost, its interpretation requires a trained pathologist. Our goal is to automate the segmentation and classification of morphological features needed to evaluate the use of Papanicolaou staining for early detection of mouth cancer. METHODS: We built a convolutional neural network (CNN) for automatic segmentation and classification of cells in Papanicolaou-stained images. Our CNN was trained and evaluated on a new image dataset of cells from oral mucosa consisting of 1,563 Full HD images from 52 patients, annotated by specialists. The effectiveness of our model was evaluated against a group of experts. Its robustness was also demonstrated on five public datasets of cervical images captured with different microscopes and cameras, and having different resolutions, colors, background intensities, and noise levels. RESULTS: Our CNN model achieved expert-level performance in a comparison with a group of three human experts on a set of 400 Papanicolaou-stained images of the oral mucosa from 20 patients. The results of this experiment exhibited high Interclass Correlation Coefficient (ICC) values. Despite being trained on images from the oral mucosa, it produced high-quality segmentation and plausible classification for five public datasets of cervical cells. Our Papanicolaou-stained image dataset is the most diverse publicly available image dataset for the oral mucosa in terms of number of patients. CONCLUSION: Our solution provides the means for exploring the potential of Papanicolaou-staining as a powerful and inexpensive tool for early detection of oral cancer. We are currently using our system to detect suspicious cells and cell clusters in oral mucosa slide images. Our trained model, code, and dataset are available and can help practitioners and stimulate research in early oral cancer detection.

A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Accurate volumetric assessment of spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a labor-intensive task performed with current manual and semiautomatic methods that might be relevant for its clinical and prognostic implications. In the present research, we sought to develop and validate an artificial intelligence-driven, fully automated blood segmentation tool for SAH patients via non-contrast computed tomography (NCCT) scans employing a transformer-based Swin-UNETR architecture. METHODS: We retrospectively analyzed NCCT scans from patients with confirmed aSAH utilizing the Swin-UNETR for segmentation. The performance of the proposed method was evaluated against manually segmented ground truth data using metrics such as Dice score, Intersection over Union (IoU), Volumetric Similarity Index(VSI), Symmetric Average Surface Distance(SASD), Sensitivity and Specificity. A validation cohort from an external institution was included to test the generalizability of the model. RESULTS: The model demonstrated high accuracy with robust performance metrics across the internal and external validation cohorts. Notably, it achieved high Dice coefficient (0.873±0.097), IoU (0.810±0.092), VSI (0.840±0.131), Sensitivity (0.821±0.217) and Specificity (0.996±0.004) values and a low SASD (1.866±2.910), suggesting proficiency in segmenting blood in SAH patients. The model's efficiency was reflected in its processing speed, indicating potential for real-time applications. CONCLUSIONS: Our Swin UNETR-based model offers significant advances in the automated segmentation of blood in SAH patients on NCCT images. Despite the computational demands, the model operates effectively on standard hardware with a user-friendly interface, facilitating broader clinical adoption. Further validation across diverse datasets is warranted to confirm its clinical reliability.

Reciprocal Inhibition and Coactivation of Ankle Muscles in Low- and High-Velocity Forward and Backward Perturbations.

Reciprocal inhibition and coactivation are strategies of the central nervous system used to perform various daily tasks. In automatic postural responses (APR), coactivation is widely investigated in the ankle joint muscles, however reciprocal inhibition, although clear in manipulative motor actions, has not been investigated in the context of APRs. The aim was to identify whether reciprocal inhibition can be observed as a strategy in the recruitment of gastrocnemius Medialis (GM), Soleus (So) and Tibialis Anterior (TA) muscles in low- and high-velocity forward and backward perturbations. We applied two balance perturbations with a low and a high velocity of displacement of the movable platform in forward and backward conditions and we evaluated the magnitude and latency time of TA, GM and So activation latency, measured by electromyography (EMG). In forward perturbations, coactivation of the three muscles was observed, with greater activation amplitude of the GM and lesser amplitude of the So and TA muscles. For backward, the pattern of response observed was activation of the TA muscle, a decrease in the EMG signal, which characterizes reciprocal inhibition of the GM muscle and maintenance of the basal state of the So muscle. This result indicates that backward perturbations are more challenging.

Predicting mortality amongst Jordanian men with heart attacks using the chi-square automatic interaction detection model.

Background: One of the most complicated cardiovascular diseases in the world is heart attack. Since men are the most likely to develop cardiac diseases, accurate prediction of these conditions can help save lives in this population. This study proposed the Chi-Squared Automated Interactive Detection (CHAID) model as a prediction algorithm to forecast death versus life among men who might experience heart attacks. Methods: Data were extracted from the electronic health solution system in Jordan using a retrospective, predictive study. Between 2015 and 2021, information on men admitted to public hospitals in Jordan was gathered. Results: The CHAID algorithm had a higher accuracy of 93.72% and an area under the curve of 0.792, making it the best top model created to predict mortality among Jordanian men. It was discovered that among Jordanian men, governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, systolic blood pressure, and pulse pressure were the most significant predicted risk factors of mortality from heart attack. Conclusion: With heart attack complaints as the primary risk factors that were predicted using machine learning algorithms like the CHAID model, demographic characteristics and hemodynamic readings were presented.

Estimation of human body 3D pose for parent-infant interaction settings using azure Kinect and OpenPose.

Automatic pose estimation has become a valuable tool for the study of human behavior, including dyadic interactions. It allows researchers to analyze the nuanced dynamics of interactions more effectively, and facilitates the integration of behavioral data with other modalities (EEG, etc.). However, many technical difficulties remain. Particularly, for parent-infant interactions, automatic pose estimation for infants is unpredictable; the immature proportions and smaller bodies of children may cause misdetections. OpenPose is one tool that has shown high performance in pose tracking from video, even in infants. However, OpenPose is limited to 2D (i.e., coordinates relative to the image space). This may be undesirable in a multitude of paradigms (e.g., naturalistic settings). We developed a method for expanding the functionality of OpenPose to 3D, tailored to parent-infant interaction paradigms. This method merges the estimations from OpenPose with the depth information from a depth camera to obtain a 3D pose that works even for young infants.•Video recordings of interactions of parents and infants are taken using a dual color-depth camera.•2D-positions of parents and their infants are estimated from the color video.•Using the depth camera, we transform the 2D estimations into real-world 3D positions, allowing movement analysis in full-3D space.

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain.

BACKGROUND: Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients. METHODS: A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis. RESULTS: The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables. CONCLUSIONS: The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic. CLINICALTRIALS: gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Cardiac Substructure Dose and Survival in Stereotactic Radiotherapy for Lung Cancer: Results of the Multi-Centre SSBROC Trial.

BACKGROUND AND PURPOSE: Stereotactic ablative body radiotherapy (SABR) is increasingly used for early-stage lung cancer, however the impact of dose to the heart and cardiac substructures remains largely unknown. The study investigated doses received by cardiac substructures in SABR patients and impact on survival. MATERIALS AND METHODS: SSBROC is an Australian multi-centre phase II prospective study of SABR for stage I non-small cell lung cancer. Patients were treated between 2013 and 2019 across 9 centres. In this secondary analysis of the dataset, a previously published and locally developed open-source hybrid deep learning cardiac substructure automatic segmentation tool was deployed on the planning CTs of 117 trial patients. Physical doses to 18 cardiac structures and EQD2 converted doses (α/ß = 3) were calculated. Endpoints evaluated include pericardial effusion and overall survival. Associations between cardiac doses and survival were analysed with the Kaplan-Meier method and Cox proportional hazards models. RESULTS: Cardiac structures that received the highest physical mean doses were superior vena cava (22.5 Gy) and sinoatrial node (18.3 Gy). The highest physical maximum dose was received by the heart (51.7 Gy) and right atrium (45.3 Gy). Three patients developed grade 2, and one grade 3 pericardial effusion. The cohort receiving higher than median mean heart dose (MHD) had poorer survival compared to those who received below median MHD (p = 0.00004). On multivariable Cox analysis, male gender and maximum dose to ascending aorta were significant for worse survival. CONCLUSIONS: Patients treated with lung SABR may receive high doses to cardiac substructures. Dichotomising the patients according to median mean heart dose showed a clear difference in survival. On multivariable analyses gender and dose to ascending aorta were significant for survival, however cardiac substructure dosimetry and outcomes should be further explored in larger studies.

Automatic measurement of anatomical parameters of the lumbar vertebral body and the intervertebral disc on radiographs by deep learning.

Background: Lumbar spine disorders are one of the common causes of low back pain (LBP). Objective and reliable measurement of anatomical parameters of the lumbar spine is essential in the clinical diagnosis and evaluation of lumbar disorders. However, manual measurements are time-consuming and laborious, with poor consistency and repeatability. Here, we aim to develop and evaluate an automatic measurement model for measuring the anatomical parameters of the vertebral body and intervertebral disc based on lateral lumbar radiographs and deep learning (DL). Methods: A model based on DL was developed with a dataset consisting of 1,318 lateral lumbar radiographs for the prediction of anatomical parameters, including vertebral body heights (VBH), intervertebral disc heights (IDH), and intervertebral disc angles (IDA). The mean of the values obtained by 3 radiologists was used as a reference standard. Statistical analysis was performed in terms of standard deviation (SD), mean absolute error (MAE), Percentage of correct keypoints (PCK), intraclass correlation coefficient (ICC), regression analysis, and Bland-Altman plot to evaluate the performance of the model compared with the reference standard. Results: The percentage of intra-observer landmark distance within the 3 mm threshold was 96%. The percentage of inter-observer landmark distance within the 3 mm threshold was 94% (R1 and R2), 92% (R1 and R3), and 93% (R2 and R3), respectively. The PCK of the model within the 3 mm distance threshold was 94-99%. The model-predicted values were 30.22±3.01 mm, 10.40±3.91 mm, and 10.63°±4.74° for VBH, IDH, and IDA, respectively. There were good correlation and consistency in anatomical parameters of the lumbar vertebral body and disc between the model and the reference standard in most cases (R2=0.89-0.95, ICC =0.93-0.98, MAE =0.61-1.15, and SD =0.89-1.64). Conclusions: The newly proposed model based on a DL algorithm can accurately measure various anatomical parameters on lateral lumbar radiographs. This could provide an accurate and efficient measurement tool for the quantitative evaluation of spinal disorders.

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine.

Background: Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance. Methods: This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model. Results: Within the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.5-0.66, RMSE =0.66-0.95). The model outperformed other models (EfficientDet + Unet, EfficientDet + DarkPose, HRNet, and Unet) in predicting landmarks within a distance threshold of 1.5 to 5 mm. Conclusions: The model developed in this study can automatically measure the morphological parameters of the L1 to L4 vertebrae from anteroposterior digital radiographs of the lumbar spine. Its performance is close to the level of radiologists.

Automation of duplicate record detection for systematic reviews: Deduplicator.

BACKGROUND: To describe the algorithm and investigate the efficacy of a novel systematic review automation tool "the Deduplicator" to remove duplicate records from a multi-database systematic review search. METHODS: We constructed and tested the efficacy of the Deduplicator tool by using 10 previous Cochrane systematic review search results to compare the Deduplicator's 'balanced' algorithm to a semi-manual EndNote method. Two researchers each performed deduplication on the 10 libraries of search results. For five of those libraries, one researcher used the Deduplicator, while the other performed semi-manual deduplication with EndNote. They then switched methods for the remaining five libraries. In addition to this analysis, comparison between the three different Deduplicator algorithms ('balanced', 'focused' and 'relaxed') was performed on two datasets of previously deduplicated search results. RESULTS: Before deduplication, the mean library size for the 10 systematic reviews was 1962 records. When using the Deduplicator, the mean time to deduplicate was 5 min per 1000 records compared to 15 min with EndNote. The mean error rate with Deduplicator was 1.8 errors per 1000 records in comparison to 3.1 with EndNote. Evaluation of the different Deduplicator algorithms found that the 'balanced' algorithm had the highest mean F1 score of 0.9647. The 'focused' algorithm had the highest mean accuracy of 0.9798 and the highest recall of 0.9757. The 'relaxed' algorithm had the highest mean precision of 0.9896. CONCLUSIONS: This demonstrates that using the Deduplicator for duplicate record detection reduces the time taken to deduplicate, while maintaining or improving accuracy compared to using a semi-manual EndNote method. However, further research should be performed comparing more deduplication methods to establish relative performance of the Deduplicator against other deduplication methods.

Automatic reorientation to generate short-axis myocardial PET images.

BACKGROUND: Accurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images. METHODS: A total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients. RESULTS: On the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications. CONCLUSION: Our algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Effects of organ dose modulation applied to a part of the scan range on radiation dose in computed tomography of the body.

In computed tomography (CT), organ dose modulation (ODM) reduces radiation exposure from the anterior side to reduce radiation dose received by the radiosensitive organs located anteriorly. We investigated the effects of ODM applied to a part of the scan range on radiation dose in body CT. The thorax and thoraco-abdominopelvic region of an anthropomorphic whole-body phantom were imaged with and without ODM. ODM was applied to various regions, and the tube current modulation curves were compared. Additionally, the dose indices were compared with and without ODM in thoracic and thoraco-abdominopelvic CTs in 800 patients. ODM was applied to the thyroid in male patients and to the thyroid and breast in female patients. In phantom imaging of the thorax, the application of ODM below the scan range decreased the tube current, and that to the breast showed a further decrease. Decreased tube current was also observed in phantom imaging of the thoraco-abdominopelvic regions with ODM below the scan range, and the application of ODM to the whole scan range, thyroid, breast, and both thyroid and breast further reduced the tube current in the region to which ODM was applied. In patient imaging, the dose indices were significantly lower with ODM than without ODM, regardless of the scan range or sex. The absolute reduction in dose-length product was larger for thoraco-abdominopelvic CT (male, 43.2 mGy∙cm; female, 59.7 mGy∙cm) than for thoracic CT (male, 30.8 mGy∙cm; female, 37.6 mGy∙cm) in both sexes, indicating dose reduction in the abdominopelvic region to which ODM was not applied. In conclusion, The application of ODM in body CT reduces radiation dose not only in the region to which ODM is applied but also outside the region. In radiation dose management, it should be considered that even ODM applied to a limited region affects the dose indices. .

Prioritization of social information processing: Eye gaze elicits earlier vMMN than arrows.

Numerous research studies have demonstrated that eye gaze and arrows act as cues that automatically guide spatial attention. However, it remains uncertain whether the attention shifts triggered by these two types of stimuli vary in terms of automatic processing mechanisms. In our current investigation, we employed an equal probability paradigm to explore the likenesses and distinctions in the neural mechanisms of automatic processing for eye gaze and arrows in non-attentive conditions, using visual mismatch negative (vMMN) as an indicator of automatic processing. The sample size comprised 17 participants. The results indicated a significant interaction between time duration, stimulus material, and stimulus type. The findings demonstrated that both eye gaze and arrows were processed automatically, triggering an early vMMN, although with temporal variations. The vMMN for eye gaze occurred between 180 and 220 ms, whereas for arrows it ranged from 235 to 275 ms. Moreover, arrow stimuli produced a more pronounced vMMN amplitude. The earlier vMMN response to eye gaze compared with arrows implies the specificity and precedence of social information processing associated with eye gaze over the processing of nonsocial information with arrows. However, arrow could potentially elicit a stronger vMMN because of their heightened salience compared to the background, and the expansion of attention focusing might amplify the vMMN impact. This study offers insights into the similarities and differences in attention processing of social and non-social information under unattended conditions from the perspective of automatic processing.

FAST-EM array tomography: a workflow for multibeam volume electron microscopy.

Elucidating the 3D nanoscale structure of tissues and cells is essential for understanding the complexity of biological processes. Electron microscopy (EM) offers the resolution needed for reliable interpretation, but the limited throughput of electron microscopes has hindered its ability to effectively image large volumes. We report a workflow for volume EM with FAST-EM, a novel multibeam scanning transmission electron microscope that speeds up acquisition by scanning the sample in parallel with 64 electron beams. FAST-EM makes use of optical detection to separate the signals of the individual beams. The acquisition and 3D reconstruction of ultrastructural data from multiple biological samples is demonstrated. The results show that the workflow is capable of producing large reconstructed volumes with high resolution and contrast to address biological research questions within feasible acquisition time frames.

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.

PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer. METHODS: A DL model was developed for predicting patient-specific doses, trained and validated on a dataset of 235 patients with diverse target volumes and prescriptions. The model was integrated into clinical workflow with DL-predicted objective functions. The automatic plans were retrospectively designed for additional 50 treated manual volumetric modulated arc therapy (VMAT) plans. A comparison was made between automatic and manual plans in terms of dosimetric indexes, monitor units (MUs) and planning time. Plan quality metric (PQM) encompassing these indexes was evaluated, with higher PQM values indicating superior plan quality. Qualitative evaluations of two plans were conducted by four reviewers. RESULTS: The PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans (P = 0.75). Compared to manual plans, the targets coverage and homogeneity of automatic plans demonstrated no significant difference. Manual plans exhibited better sparing for lung in V5 (difference: 1.8 ± 4.2 %, P = 0.02), whereas automatic plans showed enhanced sparing for heart in V30 (difference: 1.4 ± 4.7 %, P = 0.02) and for spinal cord in Dmax (difference: 0.7 ± 4.7 Gy, P = 0.04). The planning time and MUs of automatic plans were significantly reduced by 70.5 ± 20.0 min and 97.4 ± 82.1. Automatic plans were deemed acceptable in 88 % of the reviews (176/200). CONCLUSIONS: The DL-assisted approach for lung cancer notably decreased planning time and MUs, while demonstrating comparable or superior quality relative to manual plans. It has the potential to provide benefit to lung cancer patients.