Intelligent medicine最新文献

{"title":"Neuroimaging perspective in targeted treatment for type 2 diabetes melitus and sleep disorders","authors":"Karen M. von Deneen ,&nbsp;Malgorzata A. Garstka","doi":"10.1016/j.imed.2022.05.003","DOIUrl":"10.1016/j.imed.2022.05.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 4","pages":"Pages 209-220"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000390/pdfft?md5=92cde2206db2a5e2d36eea2d839fa06b&pid=1-s2.0-S2667102622000390-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42693021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nutritional and physical improvements in older adults through the DOREMI remote coaching approach: a real-world study","authors":"Federico Vozzi ,&nbsp;Filippo Palumbo ,&nbsp;Erina Ferro ,&nbsp;Karl Kreiner ,&nbsp;Franca Giugni ,&nbsp;Rachel Dutton ,&nbsp;Shirley Hall ,&nbsp;Daniele Musian ,&nbsp;Marina Parolini ,&nbsp;Patrizia Riso ,&nbsp;Oberdan Parodi","doi":"10.1016/j.imed.2022.04.001","DOIUrl":"10.1016/j.imed.2022.04.001","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Background&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;Malnutrition (excess or defect) and sedentariness act as an accelerator in the older people frailty process. A systemic solution has been developed to engage older people in a healthier lifestyle using serious games and food monitoring. The study aimed to evaluate protocol influence on variables related to unhealthy behaviors improving dietary habits through a remote nutritional coaching approach and stimulating the population to increase physical activity through Exergames.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Methods&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;Thirty-two subjects (25 Treatments and 7 Controls, aging 65–80 years), of which 15 (11 Treatments and 4 Controls) living in the UK (ACCORD and ExtraCare Villages placed in Shenley Wood (Milton Keynes), St. Crispin (Northampton), and Showell Court (Wolverhampton)) and 17 (14 Treatments and 3 Controls) in Italy (Genoa, Liguria), were recruited and characterized in terms of nutritional status, physical, somatometric, hemodynamic and biochemical measurements, and body composition. Participants were stimulated to adopt the Mediterranean dietary pattern, by a food diary diet-app, and perform regular physical activity, by the Exergame app, for three months. At the end of the trial, users underwent the same test battery. Data were tested for normality of distribution by the Shapiro-Wilk test. Comparisons between groups were performed at baseline by unpaired Student's &lt;em&gt;t&lt;/em&gt;-test for continuous variables, chi-square test, or Fisher's exact test for categorical variables. Analysis of Variance (ANOVA) for repeated measures was used to analyze the significance of changes over time between groups.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;At the end of the trial, significant reductions of systolic (15 mmHg, &lt;em&gt;P&lt;/em&gt; = 0.001), diastolic (5 mmHg, &lt;em&gt;P&lt;/em&gt; = 0.025), mean (10 mmHg, &lt;em&gt;P&lt;/em&gt; = 0.001) blood pressure, and rate-pressure product (RPP) (1,105 mmHg*bpm, &lt;em&gt;P&lt;/em&gt; = 0.017) values were observed in DOREMI users. A trend of improvement of physical performance by the short physical performance battery (SPPB) was observed for balance and walk subtests. A significant decrease (0.91 kg, &lt;em&gt;P&lt;/em&gt; = 0.043) in Body Mass Index (BMI) was observed in overweight subjects (BMI &gt;25 kg/m&lt;sup&gt;2&lt;/sup&gt;) after DOREMI intervention in the entire population. The Mini Nutritional Assessment (MNA) score (1, &lt;em&gt;P&lt;/em&gt; = 0.004) significantly increased after intervention, while waist measure (3 cm, &lt;em&gt;P&lt;/em&gt; &lt;0.001) significantly decreased in the DOREMI users. A reduction in glycated hemoglobin (Hb) was registered (0.20%, &lt;em&gt;P&lt;/em&gt; = 0.018) in the DOREMI UK users.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Conclusions&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;Improvement of healthy behavior by technological tools, providing feedback between user and remote coach and increasing user's motivation, appears potentially effective. This information and communication technologies (ICT) approach offers an","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 4","pages":"Pages 181-192"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000134/pdfft?md5=b38482bb3e373d20340a47ce05386bc5&pid=1-s2.0-S2667102622000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48679882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(22)00094-8","DOIUrl":"https://doi.org/10.1016/S2667-1026(22)00094-8","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 4","pages":"Pages 235-240"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000948/pdfft?md5=b559266c22b8ee8c493e8b774c0ee805&pid=1-s2.0-S2667102622000948-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137089737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists","authors":"Longjiang Zhang ,&nbsp;Zhao Shi ,&nbsp;Min Chen ,&nbsp;Yingmin Chen ,&nbsp;Jingliang Cheng ,&nbsp;Li Fan ,&nbsp;Nan Hong ,&nbsp;Wenxiao Jia ,&nbsp;Guihua Jiang ,&nbsp;Shenghong Ju ,&nbsp;Xiaogang Li ,&nbsp;Xiuli Li ,&nbsp;Changhong Liang ,&nbsp;Weihua Liao ,&nbsp;Shiyuan Liu ,&nbsp;Zaiming Lu ,&nbsp;Lin Ma ,&nbsp;Ke Ren ,&nbsp;Pengfei Rong ,&nbsp;Bin Song ,&nbsp;Zhengyu Jin","doi":"10.1016/j.imed.2022.07.001","DOIUrl":"10.1016/j.imed.2022.07.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 4","pages":"Pages 221-229"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000687/pdfft?md5=cbfd220c0f8f1d6f7a200fe6a89b4bea&pid=1-s2.0-S2667102622000687-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49587694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the use of cybernetics for an enhanced cervical cancer care strategy","authors":"Ejay Nsugbe","doi":"10.1016/j.imed.2022.02.001","DOIUrl":"10.1016/j.imed.2022.02.001","url":null,"abstract":"<div><h3><em><strong>Background</strong></em></h3><p>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.</p></div><div><h3><em><strong>Methods</strong></em></h3><p>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.</p></div><div><h3><em><strong>Results</strong></em></h3><p>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.</p></div><div><h3><em><strong>Conclusion</strong></em></h3><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 3","pages":"Pages 117-126"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000122/pdfft?md5=fa64c794a56a5781989a4501c20e0f60&pid=1-s2.0-S2667102622000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41341056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico analysis reveals the core targets and mechanisms of CA028, a new derivative of calycosin, in the treatment of colorectal cancer","authors":"Feiying Yin ,&nbsp;Xing Zhang ,&nbsp;Yu Li ,&nbsp;Xiao Liang ,&nbsp;Rong Li ,&nbsp;Jian Chen","doi":"10.1016/j.imed.2022.03.002","DOIUrl":"10.1016/j.imed.2022.03.002","url":null,"abstract":"<div><h3><em><strong>Background</strong></em></h3><p>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.</p></div><div><h3><em><strong>Methods</strong></em></h3><p>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.</p></div><div><h3><em><strong>Results</strong></em></h3><p>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 (F<em>YN</em>), mitogen-activated protein kinase 1 (<em>MAPK1</em>), MAPK8, <em>MAPK14</em>, Rac family small GTPase 1 (<em>RAC1</em>), epidermal growth factor receptor (<em>EGFR</em>), protein tyrosine kinase 2 (<em>PTK2</em>), sphingosine-1-phosphate receptor 1 (<em>S1PR1</em>), <em>S1PR2</em>, Janus kinase 1 (<em>JAK1</em>), <em>JAK2</em>, the RELA proto-oncogene NF-κB subunit (<em>RELA</em>), bradykinin receptor B1 (<em>BDKRB1</em>), and <em>BDKRB2</em>. 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.</p></div><div><h3><em><strong>Conclusion</strong></em></h3><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 3","pages":"Pages 127-133"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000080/pdfft?md5=b39fafac5d2e7e14f9115d2eb5e3bee7&pid=1-s2.0-S2667102622000080-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47430631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections","authors":"Yiheng Ju ,&nbsp;Longbo Zheng ,&nbsp;Peng Zhao ,&nbsp;Fangjie Xin ,&nbsp;Fengjiao Wang ,&nbsp;Yuan Gao ,&nbsp;Xianxiang Zhang ,&nbsp;Dongsheng Wang ,&nbsp;Yun Lu","doi":"10.1016/j.imed.2022.03.004","DOIUrl":"10.1016/j.imed.2022.03.004","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;&lt;strong&gt;&lt;em&gt;Background&lt;/em&gt;&lt;/strong&gt;&lt;/h3&gt;&lt;p&gt;The incidence of colorectal cancer is increasing worldwide, and it currently ranks third among all cancers. Moreover, pathological diagnosis is becoming increasingly arduous. Artificial intelligence has demonstrated the ability to fully excavate image features and assist doctors in making decisions. Large panoramic pathological sections contain considerable amounts of pathological information. In this study, we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer, as well as for T staging and prognostic analysis.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Methods&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District (internal dataset) and 42 cases from Shinan and Laoshan Hospital District (external dataset) that had tissue surgically removed from January to September 2019. After sectioning, staining, and scanning, a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained. The patients in the internal dataset were randomly divided into a training cohort (&lt;em&gt;n =&lt;/em&gt;88 ) and a test cohort (&lt;em&gt;n&lt;/em&gt; =38 ) at a ratio of 7:3. We chose DeepLabV3+ and ResNet50 as target models for our experiment. We used the Dice similarity coefficient, accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) to evaluate the performance of the artificial intelligence platform in the test set and validation set. Finally, we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;In the test set, the accuracy of image segmentation was 95.8%, the Dice coefficient was 0.92, the accuracy of automatic T-staging recognition was 86%, and the ROC AUC value was 0.93. In the validation set, the accuracy of image segmentation was 95.3%, the Dice coefficient was 0.90, the accuracy of automatic classification was 85%, the ROC AUC value was 0.92, and the image analysis time was 0.2 s. There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up. Univariate analysis showed that T stage, N stage, preoperative carcinoembryonic antigen (CEA) level, and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer. When these factors were included in a multivariate analysis, only preoperative CEA level and N stage showed significant differences.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;&lt;em&gt;&lt;strong&gt;Conclusion&lt;/strong&gt;&lt;/em&gt;&lt;/h3&gt;&lt;p&gt;The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency. Using large panoramic pathological sections enables better judgment of the condi","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 3","pages":"Pages 141-151"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000109/pdfft?md5=86b599ef05e61c30eaa22298216678a0&pid=1-s2.0-S2667102622000109-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48061599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handwashing quality assessment via deep learning: a modelling study for monitoring compliance and standards in hospitals and communities","authors":"Ting Wang ,&nbsp;Jun Xia ,&nbsp;Tianyi Wu ,&nbsp;Huanqi Ni ,&nbsp;Erping Long ,&nbsp;Ji-Peng Olivia Li ,&nbsp;Lanqin Zhao ,&nbsp;Ruoxi Chen ,&nbsp;Ruixin Wang ,&nbsp;Yanwu Xu ,&nbsp;Kai Huang ,&nbsp;Haotian Lin","doi":"10.1016/j.imed.2022.03.005","DOIUrl":"10.1016/j.imed.2022.03.005","url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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).</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 3","pages":"Pages 152-160"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000110/pdfft?md5=f88a53ded6eb66de365c818920a4d5b3&pid=1-s2.0-S2667102622000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54899881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluid–structure interaction simulation of pathological MV dynamics in a coupled MV–LV model","authors":"L. Cai, T. Zhao, Yongheng Wang, Xiaoyu Luo, Hao Gao","doi":"10.1016/j.imed.2022.06.005","DOIUrl":"https://doi.org/10.1016/j.imed.2022.06.005","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54900009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of automated International Classification of Diseases coding: development, challenges, and applications","authors":"Chenwei Yan ,&nbsp;Xiangling Fu ,&nbsp;Xien Liu ,&nbsp;Yuanqiu Zhang ,&nbsp;Yue Gao ,&nbsp;Ji Wu ,&nbsp;Qiang Li","doi":"10.1016/j.imed.2022.03.003","DOIUrl":"10.1016/j.imed.2022.03.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 3","pages":"Pages 161-173"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102622000092/pdfft?md5=46ff900f8f27606836538b7809ec824b&pid=1-s2.0-S2667102622000092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47747851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}