Computer methods and programs in biomedicine update最新文献

Background and Objective

One of the primary challenges faced by telehealth systems is the accurate transmission of patient information to remote doctors. In this context, portable medical sensors deployed at the remote patients' end play a crucial role in measuring vital information. There are many sensors available in the market. However, the accuracy of the sensors has been always a concern. The objective of this study is to verify different sensors and create awareness for using accurate sensors to avoid misdiagnosis for the patients’ safety.

Methods

This study considered the test result of a Japanese clinical pathology laboratory as the reference gold standard. The clinical pathology laboratory uses 1) Hexokinase UV method for blood glucose, 2) Enzymatic Determination method for cholesterol, 3) Automatic Analyzer (EDTA-2 K) of Hemoglobin, and 4) Uricase POD method for uric acid. To assess the performance of a medical sensor, its test results were compared to the gold standard test results obtained from the laboratory using the same sample. A Normalized Root Mean Square Error (NRMSE) threshold of less than 0.2 was established as the criterion for determining whether the medical sensor's performance fell within an acceptable range.

Results

Among the eight most commonly used blood glucose devices in the Asian market, only one device was deemed acceptable with NRMSE less than 0.2. However, all four devices found in the Japanese market showed their acceptability. In the case of cholesterol, hemoglobin, and uric acid devices, only a limited number of items were available in Asian markets. Some of the hemoglobin and uric acid devices were found to be somewhat acceptable, while all the cholesterol sensors were found erroneous.

Conclusions

This study has clearly shown the issues with the portable medical sensors and recommends the device approval authority of each country to approve sales of the quality sensors only for patients’ safety.

Purpose

Several therapeutic combinations are available for the treatment of advanced gastric cancer (AGC). It is unclear which combinations are most beneficial to the patients. The purpose of this study was to compare the efficacy and safety of Tegafur/ gimeracil/ oteracil (S-1) plus oxaliplatin (S-1OX) with capecitabine plus oxaliplatin (CAPOX) in patients with AGC.

Materials and Methods

Relevant randomized controlled trials were searched in MEDLINE, EMBASE, The Cochrane Library (CENTRAL), two major Chinese biomedical databases (CBM, CNKI), and registry centers until July 22, 2019, with no language restrictions. Data were extracted for overall response rate (ORR), time to progression (TTP), overall survival time (OST), and toxicity. The systematic review was performed according to the recommendations of the Cochrane collaboration. RevMan 5.3.1 was used for statistical analysis.

Results

A total of 6 randomized controlled trials involving 911 patients were included. The quality of the trials was less than 3 points. All the trials demonstrated a significantly improved toxicity (hand-foot syndrome and neuropathy) in the S-1OX trials (p < 0.05). There was no statistically significant difference (p > 0.05) between S-1OX versus CAPOX in terms of ORR, OST, TTP. Any of the subgroup analyses did not exhibit heterogeneity, so the fixed-effects model be used to execute the subgroup meta-analysis.

Conclusions

Both S-1OX and CAPOX showed similar efficacy for treatment of AGC. However, S1-OX appeared to present less toxicity in terms of hand-foot syndrome and neuropathy as compared to CAPOX.

Shear wave (SW) elastography is an ultrasound imaging modality that provides quantitative viscoelastic measurements of tissue. The phase difference method allows for local estimation of viscoelasticity by computing the dispersion curve using phases from two laterally-spaced pixels. However, this method is sensitive to measurement noise in the estimated SW particle velocities. Hence, we propose the delayed matrix pencil method to investigate this problem, and validated its feasibility both in-silico and in-vitro. The performance was compared with the original phase difference method and other two alternative techniques based on lowpass filtering and discrete wavelet transform denoising. The estimated viscoelastic values are summarized in box plots and followed by statistical analysis. Results from both studies show the proposed method to be more robust to noise with the smallest interquartile range in both elasticity and viscosity.

Public health surveillance systems play a crucial role in detecting and responding to disease outbreaks. Visualizations of surveillance data are important for decision-making, but little attention has been paid to the usability and interaction of such systems. In this paper, we developed a set of 10 heuristics to assess the visualization and usability of public health surveillance systems. The heuristics cover aspects of perception, cognition, and interaction. The perception deals with how the system looks in the first glance and whether it has pleasant effect on the user or otherwise. Cognition deals with the question of whether enough information is provided to use the system, while usability and interaction deal with whether the system is user-friendly in terms of the tools provided for interaction and use. We recruited a panel of experts to evaluate a set of systems using our heuristics. Results showed that there was variation in the scores of the experts' assessments, indicating the importance of multiple expert evaluations. Our heuristics provide a practical and comprehensive tool for assessing the visualization and usability of public health surveillance systems, which can lead to improved decision-making and ultimately better public health outcomes. The results suggest that the heuristic based evaluation through a panel of experts can provide meaningful results and insights into the usability aspects of public health systems. The results suggest that for some systems there can be agreement in terms of evaluation while for some other systems the experts’ opinions can vary based on the weightage and importance each expert gives to a particular aspect.

Background

Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency.

Methods

This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction.

Results

Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: (1) Diagnosis and early detection of disease; (2) Prognosis of disease course and outcomes; (3) Risk assessment of future disease; (4) Treatment response for personalised medicine; (5) Disease progression; (6) Readmission risks; (7) Complication risks; and (8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction.

Discussion

The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery.

Conclusion and recommendations

AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.

Autologous ear reconstruction is a surgical procedure performed in the case of defects of the outer ear in which the malformed anatomy is reconstructed with autologous cartilage tissue and often involves the use of surgical guides modelled from a digital reconstruction of the ear anatomy. To obtain such three-dimensional anatomy, traditional imaging methods, which are expensive and invasive, can be replaced by professional 3D scanners or low-cost commercial devices. In this context, this paper focuses on the evaluation of two devices for the acquisition of the outer ear, the Intel® RealSense D405™ (stereo camera) and the TrueDepth camera of the iPhone® 13 (structured light camera), proposing a comparison based on four parameters: accuracy, precision, deviation range and point-to-point distance, in order to assess their usability in the medical field, and in particular in the context of autologous ear reconstruction. The results show that, despite significantly different handling of the raw data, the performance of the two devices is comparable: average accuracy is 0.76 mm for the D405 and 0.95 mm for the iPhone 13, average precision is 0.071 mm for the D405 and 0.065 mm for the iPhone 13, average range of deviation is 3.12 mm for the D405 and 3.64 mm for the iPhone 13.

Pre-training and fine-tuning have emerged as a promising paradigm across various natural language processing (NLP) tasks. The effectiveness of pretrained large language models (LLM) has witnessed further enhancement, holding potential for applications in the field of medicine, particularly in the context of Traditional Chinese Medicine (TCM). However, the application of these general models to specific domains often yields suboptimal results, primarily due to challenges like lack of domain knowledge, unique objectives, and computational efficiency. Furthermore, their effectiveness in specialized domains, such as Traditional Chinese Medicine, requires comprehensive evaluation.

To address the above issues, we propose a novel domain specific TCMDA (TCM Domain Adaptation) approach, efficient pre-training with domain-specific corpus. Specifically, we first construct a large TCM-specific corpus, TCM-Corpus-1B, by identifying domain keywords and retrieving from general corpus. Then, our TCMDA leverages the LoRA which freezes the pretrained model’s weights and uses rank decomposition matrices to efficiently train specific dense layers for pre-training and fine-tuning, efficiently aligning the model with TCM-related tasks, namely TCM-GPT-7B. We further conducted extensive experiments on two TCM tasks, including TCM examination and TCM diagnosis. TCM-GPT-7B archived the best performance across both datasets, outperforming other models by relative increments of 17% and 12% in accuracy, respectively. To the best of our knowledge, our study represents the pioneering validation of domain adaptation of a large language model with 7 billion parameters in TCM domain. We will release both TCM-Corpus-1B and TCM-GPT-7B model once accepted to facilitate interdisciplinary development in TCM and NLP, serving as the foundation for further study.

Background

There is a need to explore models of eHealth literacy that serve as mediators in the relationship between health status and well-being from multidimensional perspectives among the elderly population.

Aims

To examine series models in which eHealth literacy dimensions, including awareness of sources, recognizing quality and meaning, understanding information, perceived efficiency, and validating information, serve as mediators between health status and factors related to well-being, such as financial, physical, eudaimonic, and hedonic well-being.

Methods

This cross-sectional study included 437 Israeli seniors aged 65 or above and employed the eHEALS-E scale with six dimensions to assess eHealth literacy in the first section of the questionnaire. The second section utilized a well-being scale with five categories to measure financial, physical, social, eudaimonic, and hedonic well-being. Ethical approval was obtained from the Institutional Review Board (IRB).

Results

eHealth literacy dimensions such as understanding information, awareness of sources, validating information, and recognizing quality play a crucial role in mediating the relationship between health status and different aspects of financial, social, eudaimonic and hedonic well-being.

Conclusions

Interventions and educational programs are needed to focus on enhancing eHealth literacy, specifically targeting the dimensions of understanding information, awareness of sources, validating information, and recognizing quality. By improving these eHealth literacy dimensions, individuals' financial well-being, social well-being, and overall eudaimonic and hedonic well-being can be positively influenced.

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics.