搜索结果：Gynecologic and obstetric investigation

Cybercrime and the market for cyber-related compromises are becoming attractive revenue sources for state-sponsored actors, cybercriminals and technical individuals affected by financial hardships. Due to burgeoning cybercrime on new technological frontiers, efforts have been made to assist digital forensic investigators (DFI) and law enforcement agencies (LEA) in their investigative efforts. Forensic tool innovations and ontology developments, such as the Unified Cyber Ontology (UCO) and Cyber-investigation Analysis Standard Expression (CASE), have been proposed to assist DFI and LEA. Although these tools and ontologies are useful, they lack extensive information sharing and tool interoperability features, and the ontologies lack the latest Smart City Infrastructure (SCI) context that was proposed. To mitigate the weaknesses in both solutions and to ensure a safer cyber-physical environment for all, we propose the Smart City Ontological Paradigm Expression (SCOPE), an expansion profile of the UCO and CASE ontology that implements SCI threat models, SCI digital forensic evidence, attack techniques, patterns and classifications from MITRE. We showcase how SCOPE could present complex

Background and Purpose: Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient discomfort and increase intra-fractional motion. In this study, it was investigated whether the delivery of different VMAT plans in different fractions may improve the dosimetric quality and delivery efficiency for the treatment of patients with complex tumor geometries. Materials and Methods: A direct aperture optimization algorithm was developed which allows for the simultaneous optimization of different VMAT plans to be delivered in different fractions, based on their cumulative physical dose. Each VMAT plan is constrained to deliver a uniform dose within the target volume, such that the entire treatment does not alter the fractionation scheme and is robust against inter-fractional setup errors. This approach was evaluated in-silico for ten patients with gynecological and head-and-neck cancer. Results: For all patients, fraction-variant treatments achieved better target coverage and reduced the dose to critical organs-at-risk compared to fraction-

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare's reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI's logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather t

Purpose: To develop clinically relevant test cases for commissioning Model-Based Dose Calculation Algorithms (MBDCAs) for 192Ir High Dose Rate (HDR) gynecologic brachytherapy following the workflow proposed by the TG-186 report and the WGDCAB report 372. Acquisition and Validation Methods: Two cervical cancer intracavitary HDR brachytherapy patient models were created, using either uniformly structured regions or realistic segmentation. The computed tomography (CT) images of the models were converted to DICOM CT images via MATLAB and imported into two Treatment Planning Systems (TPSs) with MBDCA capability. The clinical segmentation was expanded to include additional organs at risk. The actual clinical treatment plan was generally maintained, with the source replaced by a generic 192Ir HDR source. Dose to medium in medium calculations were performed using the MBDCA option of each TPS, and three different Monte Carlo (MC) simulation codes. MC results agreed within statistical uncertainty, while comparisons between MBDCA and MC dose distributions highlighted both strengths and limitations of the studied MBDCAs, suggesting potential approaches to overcome the challenges. Data Format a

A growing share of the world's population is being born via assisted reproductive technology (ART), including in-vitro fertilisation (IVF). However, two concerns persist. First, ART pregnancies correlate with predictors of poor outcomes at birth--and it is unclear whether this relationship is causal. Second, the emotional and financial costs associated with ART-use might exacerbate defensive medical behaviour, where physicians intervene more than necessary to reduce the risk of adverse medical outcomes and litigation. We address the challenge of identifying the pure effect of ART-use on both maternal and infant outcomes at birth by leveraging exogenous variation in the success of ART cycles. We compare the obstetric outcomes for ART-conceived births with those of spontaneously-conceived births after a failed ART treatment. Moreover, we flexibly adjust for key confounders using double machine learning. We do this using clinical registry ART data and administrative maternal and infant data from New South Wales (NSW) between 2009-2017. We find that ART slightly decreases the risk of obstetric interventions, lowering the risk of a caesarean section and increasing the rate of spontaneou

Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an experimental, agentic workflow that leverages large language models (LLMs) augmented with predefined queries and constrained tool access (structured SQL over Suricata logs and grep-based text search) to automate the first stages of alert investigation. The proposed workflow integrates queries to provide an overview of the available data, and LLM components that selects which queries to use based on the overview results, extracts raw evidence from the query results, and delivers a final verdict of the alert. Our results demonstrate that the LLM-powered workflow can investigate log sources, plan an investigation, and produce a final verdict that has a significantly higher accuracy than a verdict produced by the same LLM without the proposed workflow. By recognizing the inherent limitations of directly applying LLMs to high-volume and unstructured data, we propose combining existing investigation practices of real-

This study presents a comparative evaluation of a Variational Autoencoder (VAE) enhanced with Minimum Description Length (MDL) regularization against a Standard Autoencoder for reconstructing high-dimensional gynecological data. The MDL-VAE exhibits significantly lower reconstruction errors (MSE, MAE, RMSE) and more structured latent representations, driven by effective KL divergence regularization. Statistical analyses confirm these performance improvements are significant. Furthermore, the MDL-VAE shows consistent training and validation losses and achieves efficient inference times, underscoring its robustness and practical viability. Our findings suggest that incorporating MDL principles into VAE architectures can substantially improve data reconstruction and generalization, making it a promising approach for advanced applications in healthcare data modeling and analysis.

Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) treatment planning. However, anatomical variability, low soft-tissue contrast in CT imaging, and limited annotated datasets pose significant challenges. This study presents GynBTNet, a novel multi-stage learning framework designed to enhance segmentation performance through self-supervised pretraining and hierarchical fine-tuning strategies. Methods: GynBTNet employs a three-stage training strategy: (1) self-supervised pretraining on large-scale CT datasets using sparse submanifold convolution to capture robust anatomical representations, (2) supervised fine-tuning on a comprehensive multi-organ segmentation dataset to refine feature extraction, and (3) task-specific fine-tuning on a dedicated GYN-BT dataset to optimize segmentation performance for clinical applications. The model was evaluated against state-of-the-art methods using the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Average Surface Distance (ASD). Results: Our GynBTNet achieved superior segmentation performance, significantly outperforming

The ever-increasing workload of digital forensic labs raises concerns about law enforcement's ability to conduct both cyber-related and non-cyber-related investigations promptly. Consequently, this article explores the potential and usefulness of integrating Large Language Models (LLMs) into digital forensic investigations to address challenges such as bias, explainability, censorship, resource-intensive infrastructure, and ethical and legal considerations. A comprehensive literature review is carried out, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the use of LLMs in investigations. The review identifies current challenges within existing digital forensic processes and explores both the obstacles and the possibilities of incorporating LLMs. In conclusion, the study states that the adoption of LLMs in digital forensics, with appropriate constraints, has the potential to improve investigation efficiency, improve traceability, and alleviate the technical and judicial barriers faced by law enforcement entities.

We investigate the high energy emission activities of two bright gamma-ray pulsars, PSR~J2021+4026 and Vela. For PSR~J2021+4026, the state changes in the gamma-ray flux and spin-down rate have been observed. We report that the long-term evolution of the gamma-ray flux and timing behavior of PSR~J2021+4026 suggests a new gamma-ray flux recovery at around MJD~58910 and a flux decrease around MJD~59500. During this epoch, the staying time, the gamma-ray flux difference and spin-down rate are smaller than previous epochs in the same state. The waiting timescale of the quasi-periodic state changes is similar to the waiting timescale of the glitch events of the Vela pulsar. For the Vela pulsar, the quench of the radio pulse was in a timescale of $\sim0.2$~s after the 2016 glitch, and the glitch may disturb the structure of the magnetosphere. Nevertheless, we did not find any evidence for a long-term change in the gamma-ray emission properties using years of $Fermi$-LAT data, and therefore, no long-term magnetosphere structural change. We also conduct searching for photons above 100~GeV using 15-year $Fermi$-LAT data, and found none. Our results provide additional information for the rela

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative researc

Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for automated Artificial Intelligence(AI) interpretation. However, the reliability of such AI systems depends critically on the quality of the acquired sweeps, and little is known about how deviations from the intended protocol affect downstream predictions. In this work, we present a systematic evaluation of BSOU quality and its impact on three key AI tasks: sweep-tag classification, fetal presentation classification, and placenta-location classification. We simulate plausible acquisition deviations, including reversed sweep direction, probe inversion, and incomplete sweeps, to quantify model robustness, and we develop automated quality-assessment models capable of detecting these perturbations. To approximate real-world deployment, we simulate a feedback loop in which flagged sweeps are re-acquired, showing that such correction improves downstream task performance. Our findings highlight the sensitivity of BSOU-based AI models to acquisition variability and demonstrate that automated quality assessment can pla

Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with important implications for healthcare outcomes. Obstetrics is a high-stakes domain in which physicians counsel patients on delivery mode choices such as vaginal birth after cesarean (VBAC) and repeat cesarean section (RCS), yet counseling language remains underexplored in large-scale clinical text analysis. In this work, we analyze physician counseling language in 2,024 obstetric history and physical narratives for a rigorously defined cohort of patients for whom both VBAC and RCS were clinically viable options. To control for confounding due to medical contraindications, we first construct a VBAC-eligible cohort using structured clinical data supplemented by a large language model (LLM)-based extraction pipeline constrained to grounded, verbatim evidence from free-text narratives. We then apply a zero-shot LLM framework to categorize counseling segments into predefined framing categories capturing how physicians linguistically present delivery options. Our analysis reveals a significant difference in counseling framing distributions

The data storage requirements for deep spectral line observations with next-generation radio interferometers like the Australian Square Kilometre Array Pathfinder (ASKAP) and the Square Kilometre Array (SKA) are extremely challenging. The default strategy is to reduce data after each daily observation and stack the resulting images. Although this approach is computationally efficient, it risks propagating systematic errors and significantly degrades the final data quality. However, storage and computation requirements for a traditional way to image the entire deep dataset together are prohibitively expensive. We present an alternative \textit{uv}-grid stacking method and compare its scientific outcomes with both the traditional approach, which processes all data jointly and serves as the best-possible result, and the default image-stacking method. Our technique involves halting the standard imaging pipeline after the daily residual visibility grids are formed. These grids are then stacked and jointly deconvolved to combine many epochs of data. Using the traditional approach as a benchmark, we show that image-stacking recovers only 92\% of the true {\HI} flux. In contrast, our \text

We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8% for goal prediction and 90.9% for action prediction.

The use of automation in digital forensic investigations is not only a technological issue, but also has political and social implications. This work discusses some challenges with the implementation and acceptance of automation in digital forensic investigation, and possible implications for current digital forensic investigators. Current attitudes towards the use of automation in digital forensic investigations are examined, as well as the issue of digital investigators knowledge acquisition and retention. The argument is made for a well planned, careful use of automation going forward that allows for a more efficient and effective use of automation in digital forensic investigations while at the same time attempting to improve the overall quality of expert investigators. Targeting and carefully controlling automated solutions for beginning investigators may improve the speed and quality of investigations while at the same time letting expert digital investigators spend more time utilizing expert level knowledge required in manual phases of investigations. By considering how automated solutions are being implemented into digital investigations, investigation unit managers can inc

Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to support clinical decision-making and downstream NLP tasks. In this paper, we advance clinical section segmentation through three key contributions. First, we curate a new de-identified, section-labeled obstetrics notes dataset, to supplement the medical domains covered in public corpora such as MIMIC-III, on which most existing segmentation approaches are trained. Second, we systematically evaluate transformer-based supervised models for section segmentation on a curated subset of MIMIC-III (in-domain), and on the new obstetrics dataset (out-of-domain). Third, we conduct the first head-to-head comparison of supervised models for medical section segmentation with zero-shot large language models. Our results show that while supervised models perform strongly in-domain, their performance drops substantially out-of-domain. In contrast, zero-shot models demonstrate robust out-of-domain adaptability once hallucinated section headers are corrected. These findings underscore the importance of developing domain-specific clinical resources and h

The present work is related to the study of the nitrogen gas flow through diverging micro and nano-channels. The direct simulation Monte-Carlo (DSMC) method has been used to study the flow. The Simplified Bernoulli Trials (SBT) collision scheme has been employed to reduce the computational costs and required amounts of the computer resources. The effects of various divergence angles on flow and thermal fields have been studied for different Knudsen numbers in late-slip and early-transition regimes. The inlet-to-outlet pressure ratio has been set to 2.5 for micro and nano-channels with a uniform constant wall temperature. By analyzing the numerical results no flow separation has been found due to slip at the wall which is different than flow behavior in continuum regime. The results indicate that the viscous component has a relatively large contribution to the overall pressure drop and flow behavior. It observed that for low divergence angles the effects of pressure forces dominate the effects of shear stress and divergence angle and cause the flow to accelerate along the channel while by increasing the divergence angle and therefore the effects of flow expansion, the flow decelerat

Historically, radio-equipment has solely been used as a two-way analogue communication device. Today, the use of radio communication equipment is increasing by numerous organisations and businesses. The functionality of these traditionally short-range devices have expanded to include private call, address book, call-logs, text messages, lone worker, telemetry, data communication, and GPS. Many of these devices also integrate with smartphones, which delivers Push-To-Talk services that make it possible to setup connections between users using a two-way radio and a smartphone. In fact, these devices can be used to connect users only using smartphones. To date, there is little research on the digital traces in modern radio communication equipment. In fact, increasing the knowledge base about these radio communication devices and services can be valuable to law enforcement in a police investigation. In this paper, we investigate what kind of radio communication equipment and services law enforcement digital investigators can encounter at a crime scene or in an investigation. Subsequent to seizure of this radio communication equipment we explore the traces, which may have a forensic inte