Hasil untuk "Medicine"

N. Ferrara, K. Alitalo

T. Brennan, L. Blank, Jordan J. Cohen et al.

P. Baraniak, T. McDevitt

T. Foster, B. Puskas, B. Mandelbaum et al.

A. Kumagai, M. Lypson

L. Salvati, B. Palterer, P. Parronchi

From the Center for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC–University Medical Center Rotterdam, Rotterdam, the Netherlands (M.W.); and the Department of Respiratory Medicine, National Coordinating Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, and Claude Bernard University — both in Lyon, France (V.C.). Address reprint requests to Dr. Wijsenbeek at the Center for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, the Netherlands, or at m . wijsenbeek-lourens @ erasmusmc . nl.

Ke Wan, Kensuke Tanioka, Toshio Shimokawa

Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistical evidence for precision medicine. While most existing methods focus on binary treatment situations, real-world applications often involve multiple interventions. However, current HTE estimation methods are primarily designed for binary comparisons and often rely on black-box models, which limit their applicability and interpretability in multi-arm settings. To address these challenges, we propose an interpretable machine learning framework for HTE estimation in multi-arm trials. Our method employs a rule-based ensemble approach consisting of rule generation, rule ensemble, and HTE estimation, ensuring both predictive accuracy and interpretability. Through extensive simulation studies and real data applications, the performance of our method was evaluated against state-of-the-art multi-arm HTE estimation approaches. The results indicate that our approach achieved lower bias and higher estimation accuracy compared with those of existing methods. Furthermore, the interpretability of our framework allows clearer insights into how covariates influence treatment effects, facilitating clinical decision making. By bridging the gap between accuracy and interpretability, our study contributes a valuable tool for multi-arm HTE estimation, supporting precision medicine.

Penglei Sun, Yixiang Chen, Xiang Li et al.

In recent years, accurately and quickly deploying medical large language models (LLMs) has become a trend. Among these, retrieval-augmented generation (RAG) has garnered attention due to rapid deployment and privacy protection. However, the challenge hinder the practical deployment of RAG for medical diagnosis: the semantic gap between colloquial patient descriptions and the professional terminology within medical knowledge bases. We try to address the challenge from the data perspective and the method perspective. First, to address the semantic gap in existing knowledge bases, we construct DiagnosGraph, a generalist knowledge graph covering both modern medicine and Traditional Chinese Medicine. It contains 876 common diseases with the graph of 7,997 nodes and 37,201 triples. To bridge the gap between colloquial patient narratives and academic medical knowledge, DiagnosGraph also introduces $1,908$ medical record by formalizing the patient chief complaint and proposing a medical diagnosis. Second, we introduce the Multi-Round Diagnostic RAG (MRD-RAG) framework. It utilizes a multi-round dialogue to refine diagnostic possibilities, emulating the clinical reasoning of a physician. Experiments conducted on four medical benchmarks, with evaluations by human physicians, demonstrate that MRD-RAG enhances the diagnostic performance of LLMs, highlighting its potential to make automated diagnosis more accurate and human-aligned.

Atheendar S. Venkataramani, Charles F. Manski, John Mullahy

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not be the case in real-world settings. Across a wide range of settings, ranging from clinical practice to education policy, demographic attributes (e.g., age, race, ethnicity) are often classified differently in research studies than in decision settings. This paper studies identification in such settings. We propose a formal framework for prediction with what we term differential covariate classification (DCC). Using this framework, we analyze partial identification of probabilistic predictions and assess how various assumptions influence the identification regions. We apply the findings to a range of settings, focusing mainly on differential classification of individuals' race and ethnicity in clinical medicine. We find that bounds on P(y|x) can be wide, and the information needed to narrow them available only in special cases. These findings highlight an important problem in using evidence in decision making, a problem that has not yet been fully appreciated in debates on classification in public policy and medicine.

Philip Schaten, Moritz Blumenthal, Bernhard Rapp et al.

Purpose: To create modular solutions for interactive real-time MRI using reconstruction algorithms implemented in BART. Methods: A new protocol for streaming of multidimensional arrays is presented and integrated into BART. The new functionality is demonstrated using examples for cardiac interactive real-time MRI based on radial FLASH, where iterative reconstruction is combined with advanced features such as dynamic coil compression and gradient-delay orrection. We analyze the latency of the reconstruction and measure end-to-end latency of the full imaging process. Results: Reconstruction pipelines with iterative reconstruction and advanced functionality were built in a modular way using scripting. Latency measurements demonstrate latency sufficient for interactive real-time MRI, on the order of 30 ms for BART processing and network transfer time, or 200 ms for end-to-end latency including acquisition, vendor processing, and display. Conclusion: With the new streaming capabilities, real-time reconstruction pipelines can be assembled using BART in a flexible way, enabling rapid prototyping of advanced applications such as interactive real-time MRI.

Yi Zhou, Yuhao Deng, Yu-Shi Tian et al.

In precision medicine, deriving the individualized treatment rule (ITR) is crucial for recommending the optimal treatment based on patients' baseline covariates. The covariate-specific treatment effect (CSTE) curve presents a graphical method to visualize an ITR within a causal inference framework. Recent advancements have enhanced the causal interpretation of the CSTE curves and provided methods for deriving simultaneous confidence bands for various study types. To facilitate the implementation of these methods and make ITR estimation more accessible, we developed CSTEapp, a web-based application built on the R Shiny framework. CSTEapp allows users to upload data and create CSTE curves through simple point and click operations, making it the first application for estimating the ITRs. CSTEapp simplifies the analytical process by providing interactive graphical user interfaces with dynamic results, enabling users to easily report optimal treatments for individual patients based on their covariates information. Currently, CSTEapp is applicable to studies with binary and time-to-event outcomes, and we continually expand its capabilities to accommodate other outcome types as new methods emerge. We demonstrate the utility of CSTEapp using real-world examples and simulation datasets. By making advanced statistical methods more accessible, CSTEapp empowers researchers and practitioners across various fields to advance precision medicine and improve patient outcomes.

Yijun Yang, Zhao-Yang Wang, Qiuping Liu et al.

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.

Gonçalo Hora de Carvalho

Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synthetic biology. Bioelectric signals, defined as voltage gradients across cell membranes caused by ionic movements, play a role in regulating crucial processes including cellular differentiation, proliferation, apoptosis, and tissue morphogenesis. Recent studies demonstrate the ability to modulate these signals to achieve controlled tissue regeneration and morphological outcomes in organisms such as planaria and frogs. However, significant knowledge gaps remain, particularly in predicting and controlling the spatial and temporal dynamics of membrane potentials (V_mem), understanding their regulatory roles in tissue and organ development, and exploring their therapeutic potential in diseases. In this work we propose an experiment using Deep Reinforcement Learning (DRL) framework together with lab automation techniques for real-time manipulation of bioelectric signals to guide tissue regeneration and morphogenesis. The proposed framework should interact continuously with biological systems, adapting strategies based on direct biological feedback. Combining DRL with real-time measurement techniques -- such as optogenetics, voltage-sensitive dyes, fluorescent reporters, and advanced microscopy -- could provide a comprehensive platform for precise bioelectric control, leading to improved understanding of bioelectric mechanisms in morphogenesis, quantitative bioelectric models, identification of minimal experimental setups, and advancements in bioelectric modulation techniques relevant to regenerative medicine and cancer therapy. Ultimately, this research aims to utilize bioelectric signaling to develop new biomedical and bioengineering applications.

Anna Wajs-Bonikowska, Ewa Maciejczyk, Łukasz Szoka et al.

This study investigates the essential oil (EO) isolated from the seeds and cones of Canadian hemlock (<i>Tsuga canadensis</i>), highlighting notable differences in their chemical composition and biological activities. The seed EO was uniquely dominated by oxygenated derivatives of monoterpene hydrocarbons, particularly bornyl acetate (40%), whereas the cone EO exhibited higher levels of monoterpene hydrocarbons such as α-pinene (23%), β-pinene (20%), and myrcene (23%). A significant finding was the strong cytotoxic activity of cone EO against melanoma cell lines, with IC₅₀ values as low as 0.104 ± 0.015 μL/mL, compared to the minimal effects of seed EO. Additionally, cone EO demonstrated stronger antimicrobial activity, with lower minimum inhibitory concentrations (MICs) against Gram-positive and Gram-negative bacteria, further highlighting its therapeutic potential. Lipophilic extracts from seeds were characterized by unsaturated fatty acids (linoleic, oleic, and sciadonic acids—specific to conifers) and bioactive molecules with high antioxidant and nutritional potential, such as β-tocopherol, β-sitosterol, and campestrol. These findings underscore the unique chemical composition of <i>T. canadensis</i> seed EO and its lipophilic extract, along with the potent cytotoxic and antimicrobial properties of cone EO, offering insights into their potential applications in natural products for pharmaceutical and therapeutic uses.

Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens et al.

Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI. Materials and methods Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models. Results All models obtained a good DSC (DSC > = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models. Conclusion The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. Clinical trial number Not applicable.

Svyatoslav M. Yurin, Dmitry A. Apalkov, Tatiana A. Minenkova et al.

Background. Congenital disorders of sex development (DSD) represent a heterogeneous group of dysontogenetic conditions characterized by a discordance between chromosomal, gonadal, and phenotypic sex. Among them, idiopathic forms of male pseudohermaphroditism with a 46,XY karyotype are among the most diagnostically challenging variants, as the presence of testicular tissue is accompanied by incomplete masculinization of the external genitalia in the absence of detectable mutations within the androgen-regulatory gene system. Given the clinico-endocrinological ambiguity of this pathology, an integrative diagnostic strategy combining hormonal, cytogenetic, and morphofunctional assessments, along with the timely determination of optimal timing for surgical and hormonal correction, acquires particular clinical importance. Objective. To perform a detailed clinico-endocrinological characterization of the idiopathic variant of DSD in a prepubertal child with a 46,XY karyotype and to determine the principles of rational diagnostic and therapeutic management. Materials and methods. The study is based on the clinical observation of a 9-year-old boy examined in the Endocrinology Department of the Kursk Regional Children’s Clinical Hospital. The analysis included medical history, physical status, serum levels of LH, FSH, testosterone, and anti-Müllerian hormone, cytogenetic data, and ultrasonographic features of the gonads and pelvic organs, correlated with up-to-date literature sources. Conclusions. Idiopathic forms of male pseudohermaphroditism with a normal male karyotype require a multidisciplinary approach and prolonged follow-up. Early surgical correction and subsequent hormonal monitoring contribute to the formation of an adequate phenotypic outcome, reduction of endocrine complications, and improvement of psychosocial adaptation during puberty.

Yakun Zhu, Shaohang Wei, Xu Wang et al.

Integrating tools into Large Language Models (LLMs) has facilitated the widespread application. Despite this, in specialized downstream task contexts, reliance solely on tools is insufficient to fully address the complexities of the real world. This particularly restricts the effective deployment of LLMs in fields such as medicine. In this paper, we focus on the downstream tasks of medical calculators, which use standardized tests to assess an individual's health status. We introduce MeNTi, a universal agent architecture for LLMs. MeNTi integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. Specifically, it achieves flexible tool selection and nested tool calling to address practical issues faced in intricate medical scenarios, including calculator selection, slot filling, and unit conversion. To assess the capabilities of LLMs for quantitative assessment throughout the clinical process of calculator scenarios, we introduce CalcQA. This benchmark requires LLMs to use medical calculators to perform calculations and assess patient health status. CalcQA is constructed by professional physicians and includes 100 case-calculator pairs, complemented by a toolkit of 281 medical tools. The experimental results demonstrate significant performance improvements with our framework. This research paves new directions for applying LLMs in demanding scenarios of medicine.

Haiqiao Wang, Hong Wu, Zhuoyuan Wang et al.

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention of PCa.To facilitate physicians with more accurate and efficient computer-assisted diagnosis and interventions, many image processing algorithms in TRUS have been proposed and achieved state-of-the-art performance in several tasks, including prostate gland segmentation, prostate image registration, PCa classification and detection, and interventional needle detection. The rapid development of these algorithms over the past two decades necessitates a comprehensive summary. In consequence, this survey provides a \textcolor{blue}{narrative } analysis of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions. Furthermore, this survey discusses current challenges and suggests future research directions to possibly advance this field further.

Lanqiu Yao, Thaddeus Tarpey

A core component of precision medicine research involves optimizing individualized treatment rules (ITRs) based on patient characteristics. Many studies used to estimate ITRs are longitudinal in nature, collecting outcomes over time. Yet, to date, methods developed to estimate ITRs often ignore the longitudinal structure of the data. Information available from the longitudinal nature of the data can be especially useful in mental health studies. Although treatment means might appear similar, understanding the trajectory of outcomes over time can reveal important differences between treatments and placebo effects. This longitudinal perspective is especially beneficial in mental health research, where subtle shifts in outcome patterns can hold significant implications. Despite numerous studies involving the collection of outcome data across various time points, most precision medicine methods used to develop ITRs overlook the information available from the longitudinal structure. The prevalence of missing data in such studies exacerbates the issue, as neglecting the longitudinal nature of the data can significantly impair the effectiveness of treatment rules. This paper develops a powerful longitudinal trajectory-based ITR construction method that incorporates baseline variables, via a single-index or biosignature, into the modeling of longitudinal outcomes. This trajectory-based ITR approach substantially minimizes the negative impact of missing data compared to more traditional ITR approaches. The approach is illustrated through simulation studies and a clinical trial for depression, contrasting it with more traditional ITRs that ignore longitudinal information.

Zhonghai Wang, Jie Jiang, Yibing Zhan et al.

Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and there is a need for in-depth study of LLMs in specific fields like anesthesiology. To fill the gap, we introduce Hypnos, a Chinese Anesthesia model built upon existing LLMs, e.g., Llama. Hypnos' contributions have three aspects: 1) The data, such as utilizing Self-Instruct, acquired from current LLMs likely includes inaccuracies. Hypnos implements a cross-filtering strategy to improve the data quality. This strategy involves using one LLM to assess the quality of the generated data from another LLM and filtering out the data with low quality. 2) Hypnos employs a general-to-specific training strategy that starts by fine-tuning LLMs using the general medicine data and subsequently improving the fine-tuned LLMs using data specifically from Anesthesiology. The general medical data supplement the medical expertise in Anesthesiology and enhance the effectiveness of Hypnos' generation. 3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology. Our benchmark includes both publicly available instances from the Internet and privately obtained cases from the Hospital. Hypnos outperforms other medical LLMs in anesthesiology in metrics, GPT-4, and human evaluation on the benchmark dataset.