Journal of Medical Systems最新文献

Blepharoptosis is a common eyelid disorder that impairs both vision and appearance, requiring accurate assessment for effective treatment. This study aimed to develop and evaluate a deep learning (DL)-based system for automatic measurement of eyelid and periorbital parameters and for classifying levator function (LF) in patients with blepharoptosis. We retrospectively included 1,177 patients who underwent ptosis surgery at a tertiary oculoplastic center from January 2016 to November 2021. LF was categorized into good (> 10 mm), fair (4-10 mm), and poor (≤ 4 mm) based on clinical evaluation. The DL model segmented eyelid and eyebrow regions and measured key parameters; manual measurements were performed for comparison. A multinomial logistic regression model incorporating DL-derived features and demographic data was used to predict LF grades. The DL system achieved high segmentation performance (Dice coefficient = 0.910) and strong agreement with manual measurements (ICC = 0.988 for MRD1; 0.902 for CBH). The regression model classified LF grades with an overall accuracy of 0.760 and an AUC of 0.829, within the range of ophthalmologist assessments (highest clinician accuracy = 0.767). This DL-based system offers an efficient, objective tool for periorbital assessment and LF grading, supporting personalized diagnosis and surgical planning in blepharoptosis management.

Risk communication represents a nuanced discourse within the healthcare sector, characterized by the topics' sensitivity and the potential for misunderstandings between healthcare providers and patients. This delicacy stems from the complexity of effectively conveying information about risks. Consequently, a primary obstacle lies in fostering healthcare providers' understanding of implicit communication nuances inherent in pre-operative risk discussions. This study aims to address this gap in the literature by examining the topic through the lens of the philosophy of language, specifically utilizing pragmatic analysis tools to elucidate implicit understandings in doctor-patient interactions. We employ this approach to scrutinize instances of pancreatic cancer diagnosis. Through empirical analysis of gathered data, we illustrate the inadequacies of current state-of-the-art models in accurately identifying misunderstandings within healthcare dialogues. In conclusion, we propose avenues for future research in this domain, underscoring the importance of further exploration into improving risk communication in healthcare settings.

Heart rate variability (HRV) is a well-established, noninvasive measure of autonomic nervous system activity and is associated with clinical outcomes. Although real-time monitoring of HRV is valuable in clinical practice, its effectiveness is often compromised by major challenges: high inter-individual variability and frequent data contamination from procedural artifacts. To address these challenges, we developed and validated a computational framework for robust and personalized real-time HRV analysis oriented toward clinical application. The framework performs simultaneous analysis and visualization of both time- and frequency-domain HRV indices and incorporates an adaptive alert algorithm that personalizes alert thresholds using the interquartile range of each patient's own data. A workflow-integrated mechanism for manually annotating and excluding artifact-prone periods prevents procedural artifacts from skewing the statistical baselines, and a multi-scale visualization module provides a unified view of short-term fluctuations and long-term trends. While existing HRV tools are powerful for research or offline analysis, they often lack the integration of personalized alerting and workflow-oriented artifact management needed for bedside care. The proposed system uniquely combines personalized alerting, care-linked artifact exclusion, and multi-scale bedside visualization within a single real-time software package. The framework was validated using open-access electrocardiogram (ECG) databases and synthetic noise-contaminated signals, confirming robust R-wave detection across pediatric and adult recordings and under low signal-to-noise conditions. In addition, the framework was operationally validated at the bedside using ECG data from 24 newborn patients. By systematically addressing the core challenges of personalization and artifact management in a clinically integrated manner, this work represents a significant step toward translating real-time HRV analysis into routine vital sign management and, ultimately, improved patient outcomes.

The ubiquitous availability of work-related applications on personal devices makes healthcare workers prone to working during leisure time. We tested the hypothesis that an intervention to reduce work-related screen time during a weekend off reduces stress in healthcare workers. Pragmatic parallel design randomized controlled trial between November 2021 and November 2023. Healthcare workers using a smartphone with a work email application were eligible. Randomization was 1:1 to no treatment or a threefold educational intervention to: 1) activate automated responses to emails received, 2) reduce screen time, and 3) uninstall work applications from personal devices. The primary outcome was the change in participants' stress from pre- to post-weekend, measured with the Perceived Stress Scale-10. The secondary outcome was device screen time. Among 815 enrolled participants, 520 responded to the post-intervention survey. The median [Q1, Q3] change from baseline Perceived Stress Scale-10 scores was -2 [-7, 0] in controls and -4 [-9, 0] in the intervention group. The mean difference (intervention - control) in post-intervention Perceived Stress Scale-10 scores, adjusted for baseline stress, was -1.6 (95% CI: -2.6, -0.6; P = 0.002). The median [Q1, Q3] change from baseline screen time was 0 [-2, 1] hours in the controls and -1 [-3, 0] hours in the intervention group. A three-pronged educational intervention targeting work-related screen time among healthcare workers doubled stress reduction during a non-work weekend. Stress reduction in the intervention group was mediated by reduced screen time. Future research should investigate long-term effects and broader implementation of such interventions to promote well-being in the healthcare workforce. Trial Registration: https://clinicaltrials.gov/study/NCT05106647 . Identifier: NCT05106647, Registration date: November 4, 2021.

Causal networks provide a mechanistic understanding of clinical phenomena, allowing for personalized and explainable decision-making. Causal discovery, namely the task of constructing such models, is challenging, particularly for rare diseases, where observational data are sparse, medical knowledge is incomplete, and diseases develop over time. This work proposes a new and original expert-in-the-loop causal discovery workflow that iteratively refines a set of causal networks associated with different disease mechanisms. When applied to soft tissue sarcoma, a heterogeneous group of rare cancers, the workflow allows for the first comprehensive causal description of the disease's natural history. Indeed, three causal networks associated with different disease mechanisms shed light on the complex interplay between patients' covariates and disease behavior. These results have the potential to enhance clinical decision-making by allowing the development of personalized treatment strategies. The proposed workflow paves the way to agile, modular, and flexible causal discovery for clinical domains characterized by data sparsity, longitudinal dynamics, and heterogeneous expert knowledge.

Software as a Medical Device (SaMD) has become indispensable in diagnostics, treatment planning, and patient monitoring. While high-income countries have introduced clear regulatory frameworks, Bangladesh and many low- and middle-income countries (LMICs) still lack tailored pathways for medical software approval (IMDRF. Software as a Medical Device (SaMD): Key Definitions (IMDRF/SaMD WG/N10FINAL:(2013)); IMDRF. Software as a Medical Device (SaMD): Clinical Evaluation (IMDRF/SaMD WG/N41FINAL:(2017)); U.S. Food and Drug Administration (FDA). Software as a Medical Device (SAMD): Clinical Evaluation Guidance for Industry and FDA Staff (2017)). The current reliance on manual processes designed for physical devices leads to inefficiencies, inconsistent decisions, and potential risks to patient safety. This Comment proposes a semi-automated, risk-based intake roadmap for Bangladesh's Directorate General of Drug Administration (DGDA). Drawing on IMDRF, EU MDCG, and U.S. FDA frameworks, it presents a tangible workflow showing which submissions can be automatically triaged, which require human review, and where human override is maintained (European Commission (MDCG). Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 (MDCG 2019-11) and World Health Organization (WHO) Global Model Regulatory Framework for medical devices including IVDs (draft; WHO) (n.d.)). Key intake fields, escalation rules, and measurable performance indicators are defined. Anchored to Bangladesh's current DGDA and national digital health context, the proposal identifies specific legal and infrastructural gaps and outlines steps for phased modernization that may guide other LMICs.