Intelligent medicine最新文献

Type 2 diabetes mellitus (T2DM) and sleep disorders (SD) have become important and costly health issues worldwide, particularly in China. Both are common diseases related to brain functional and structural abnormalities involving the hypothalamic-pituitary-adrenal (HPA) axis. The brains of individuals who suffer from both diseases simultaneously might be different compared to healthy individuals. This review assessed current neuroimaging findings to develop alternative targeted treatments for T2DM and SD. Relevant articles published between January 2002 and September 2021 were searched in PubMed and Web of Science databases. Generalized treatment methods for T2DM include dietary/weight-loss management, metformin or a combination of two non-insulin drugs, and melatonin for SD, though alternative therapies including electroacupuncture (EA) have been utilized in treating both of these diseases separately because they are convenient, affordable, and safe. Standard and alternative treatments for T2DM were somehow effective in treating SD. Neuroimaging studies of these disorders can achieve higher treatment efficacy by targeting brain areas, such as the hypothalamus (HYP), as visualized via diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI). DTI and fMRI can map the human brain and are utilized in many experiments. Thus, we propose that neuroimaging studies could be used in treatment of SD in T2DM.

In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.

Background

Cervical cancer is a prominent disease in women, with a high mortality rate worldwide. This cancer continues to be a challenge to concisely diagnose, especially in its early stages. The aim of this study was to propose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies.

Methods

In this work, we applied machine learning (ML) models on 650 patients’ data collected from Hospital Universitario de Caracas in Caracas, Venezuela, where ethical approval and informed consent were granted. The data were hosted at the University of California at Irvine (UCI) database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer. Two contrasting methods are explored in the design of a ML-driven prediction machine in this study, namely, a probabilistic method using Gaussian mixture models (GMM), and fuzziness-based reasoning using the fuzzy c-means (FCM) clustering on the data from 650 patients.

Results

The models were validated using a K-Fold validation method, and the results show that both methods could be feasibly deployed in a clinical setting, with the probabilistic method (produced accuracies of 80+%/classifier dependent) allowing for more detail in the grading of a potential cervical cancer prediction, albeit at the cost of greater computation power; the FCM approach (produced accuracies around 90+%/classifier dependent) allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison. As part of the novelty of this work, a clinical cybernetic system is also proposed to host the prediction machine, which allows for a human-machine collaborative interaction and an enhanced decision support platform to augment overall care strategies.

Conclusion

The present study showcased how the use of prediction machines can contribute towards early detection and prioritised care of patients with cervical cancer, while also allowing for cost-saving benefits when compared with routine cervical cancer screening. Further work in this area would now involve additional validation of the proposed clinical cybernetic loop and further improvement to the prediction machine by exploring non-linear dimensional embedding and clustering methods.

Background

Colorectal cancer (CRC) is a type of malignant gastroenteric tumors associated with a high mortality rate worldwide. Calycosin, a natural phytoestrogen, possesses potent anti-cancer properties. We structurally modified calycosin to improve its physicochemical properties, and generated a novel small molecule termed CA028.

Methods

By using network pharmacology, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis and molecular docking, we aimed to predict and disclose the biological functions and mechanism of CA028 in the treatment of CRC through bioinformatic analyses.

Results

By searching the online Swiss Target Prediction and TargetNet databases, we identified 150 genes shared by CA028 and CRC. Using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software, we identified 14 hub-functional genes, namely the FYN proto-oncogene, a Src family tyrosine kinase (FYN), mitogen-activated protein kinase 1 (MAPK1), MAPK8, MAPK14, Rac family small GTPase 1 (RAC1), epidermal growth factor receptor (EGFR), protein tyrosine kinase 2 (PTK2), sphingosine-1-phosphate receptor 1 (S1PR1), S1PR2, Janus kinase 1 (JAK1), JAK2, the RELA proto-oncogene NF-κB subunit (RELA), bradykinin receptor B1 (BDKRB1), and BDKRB2. Additionally, biological docking analysis using the Autodock Vina software revealed that FYN and MAPK1 were the main pharmacological proteins of CA028 against CRC. The gene ontology analysis using R-language packages further revealed the anti-CRC functions of CA028, including biological processes, cell components, and molecular pathways.

Conclusion

CA028 exhibits effective pharmacological activity against CRC by suppressing the proliferation of CRC cells and improving the tumor microenvironment. Importantly, certain predicted genes (e.g., FYN and MAPK1) may be the pharmacological targets of CA028 in the treatment of CRC.

Background

Hand hygiene can be a simple, inexpensive, and effective method for preventing the spread of infectious diseases. However, a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging. The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.

Methods

We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks (3D CNNs) and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation. An assessment scheme with a probability smoothing method was also proposed to optimize the neural network's output to identify the handwashing steps, measure the exact duration, and grade the standard level of recognized steps. Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.

Results

Using a deep learning algorithm and an assessment scheme, combined with a probability smoothing method, each handwashing step was recognized (ACC ranged from 90.64% to 98.87% in the hospital and from 87.39% to 96.71% in the community). An assessment scheme measured each step's exact duration, and the intraclass correlation coefficients were 0.98 (95% CI: 0.97–0.98) and 0.91 (95% CI: 0.88–0.93) for the total video duration in the hospital and community, respectively. Furthermore, the system assessed the quality of handwashing, similar to the expert panel (kappa = 0.79 in the hospital; kappa = 0.65 in the community).

Conclusions

This work developed an algorithm to directly assess handwashing compliance and quality from videos, which is promising for application in healthcare settings and communities to reduce pathogen transmission.

The International Classification of Diseases (ICD) is an international standard and tool for epidemiological investigation, health management, and clinical diagnosis with a fundamental role in intelligent medical care. The assignment of ICD codes to health-related documents has become a focus of academic research, and numerous studies have developed the process of ICD coding from manual to automated work. In this survey, we review the developmental history of this task in recent decades in depth, from the rules-based stage, through the traditional machine learning stage, to the neural-network-based stage. Various methods have been introduced to solve this problem by using different techniques, and we report a performance comparison of different methods on the publicly available Medical Information Mart for Intensive Care dataset. Next, we summarize four major challenges of this task: (1) the large label space, (2) the unbalanced label distribution, (3) the long text of documents, and (4) the interpretability of coding. Various solutions that have been proposed to solve these problems are analyzed. Further, we discuss the applications of ICD coding, from mortality statistics to payments based on disease-related groups and hospital performance management. In addition, we discuss different ways of considering and evaluating this task, and how it has been transformed into a learnable problem. We also provide details of the commonly used datasets. Overall, this survey aims to provide a reference and possible prospective directions for follow-up research work.