Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Cardiology

## A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features.

#### In Clinical epigenetics BACKGROUND : Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved some progress, they were based on clinical or genetic features alone. Here, we have developed a deep learning framework, HFmeRisk, using both 5 clinical features and 25 DNA methylation loci to predict the early risk of HFpEF in the Framingham Heart Study Cohort.RESULTS : The framework incorporates Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting-based feature selection, as well as a Factorization-Machine based neural network-based recommender system. Model discrimination and calibration were assessed using the AUC and Hosmer-Lemeshow test. HFmeRisk, including 25 CpGs and 5 clinical features, have achieved the AUC of 0.90 (95% confidence interval 0.88-0.92) and Hosmer-Lemeshow statistic was 6.17 (P = 0.632), which outperformed models with clinical characteristics or DNA methylation levels alone, published chronic heart failure risk prediction models and other benchmark machine learning models. Out of them, the DNA methylation levels of two CpGs were significantly correlated with the paired transcriptome levels (R < -0.3, P < 0.05). Besides, DNA methylation locus in HFmeRisk were associated with intercellular signaling and interaction, amino acid metabolism, transport and activation and the clinical variables were all related with the mechanism of occurrence of HFpEF. Together, these findings give new evidence into the HFmeRisk model.CONCLUSION : Our study proposes an early risk assessment framework for HFpEF integrating both clinical and epigenetic features, providing a promising path for clinical decision making.Zhao Xuetong, Sui Yang, Ruan Xiuyan, Wang Xinyue, He Kunlun, Dong Wei, Qu Hongzhu, Fang Xiangdong2022-Jan-19DNA methylation, Deep learning, Early risk prediction, Heart failure with preserved ejection fraction

Internal Medicine

General

General

General

General

General

General

General

General

General

Public Health

Public Health

Public Health

General

General

General

General

General

General

General

General

Surgery

General

Public Health

Cardiology

General

General

Oncology

Surgery

General

General

General

General

## [Advances in gastroenterology and hepatology 2021].

#### In Revue medicale suisse Among the recent advances in gastroenterology, colonoscopy with artificial intelligence is associated with a better quality of screening. In refractory UC, Ozanimod seems to be an interesting salvage treatment, which still needs to be validated by Swissmedic. Among the direct-acting anticoagulants, Rivaroxaban is more frequently associated with GI bleeding. The classification of oesophageal motor disorders has been recently revised, the Chicago v4.0 classification should be applied in diagnostic management. The use of Semaglutide seems to show very promising results in the management of metabolic steatosis. SARS-CoV-2 infection can be complicated by biliary tract disease, which can progress to hepatocellular failure.Bastid Caroline, Bronstein Nathan, Ghassem-Zadeh Sahar, Flattet Yves, Gressot Pablo, Mathys Philippe, Spahr Laurent, Frossard Jean-Louis2022-Jan-19

Internal Medicine

General

Pathology

General

General

General

## Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.

#### In BioMed research international ; h5-index 102.0 Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.Kasinathan Gopi, Jayakumar Selvakumar2022

Internal Medicine

General

General

General

General

General

General

General

General

Oncology

General

General

Surgery

General

General

General

General

Pathology

General

General

General

General

General

General

General

General

## Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition.

#### In Computational intelligence and neuroscience The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.Tang Chao, Tong Anyang, Zheng Aihua, Peng Hua, Li Wei2022

Internal Medicine

General

General

General

General

General

General

General

General

General

General

General

General

Cardiology

Oncology

Public Health

Ophthalmology

Public Health

Cardiology

General

General

General

General

Public Health

Surgery

Pathology

Oncology

General

General

General

General

General

General

General

General

Pathology

General

General

General

Surgery

Public Health

General

General

General

General

## Computerized Characterization of Spinal Structures on MRI and Clinical Significance of 3D Reconstruction of Lumbosacral Intervertebral Foramen.

#### In Pain physician ; h5-index 45.0 BACKGROUND : Segmentation of spinal structures is important in medical imaging analysis, which facilitates surgeons to plan a preoperative trajectory for the transforaminal approach. However, manual segmentation of spinal structures is time-consuming, and studies have not explored automatic segmentation of spinal structures at the L5/S1 level.OBJECTIVES : This study sought to develop a new method based on a deep learning algorithm for automatic segmentation of spinal structures. The resulting algorithm may be used to rapidly generate a precise 3D lumbosacral intervertebral foramen model to assist physicians in planning an ideal trajectory in L5/S1 lumbar transforaminal radiofrequency ablation (LTRFA).STUDY DESIGN : This was an observational study for developing a new technique on spinal structures segmentation.STUDY SITE : The study was carried out at the department of radiology and spine surgery at our hospital.METHODS : A total of 100 L5/S1 level data samples from 100 study patients were used in this study. Masks of vertebral bone structures (VBSs) and intervertebral discs (IVDs) for all data samples were segmented manually by a skilled surgeon and served as the "ground truth." After data preprocessing, a 3D-UNet model based on deep learning was used for automated segmentation of lumbar spine structures at L5/S1 level magnetic resonance imaging (MRI). Segmentation performances and morphometric measurement were used for 3D lumbosacral intervertebral foramen (LIVF) reconstruction  generated by either manual segmentation and automatic segmentation.RESULTS : The 3D-UNet model showed high performance in automatic segmentation of lumbar spinal structures (VBSs and IVDs). The corresponding mean Dice similarity coefficient (DSC) of 5-fold cross-validation scores for L5 vertebrae, IVDs, S1 vertebrae, and all L5/S1 level spinal structures were 93.46 ± 2.93%, 90.39 ± 6.22%, 93.32 ± 1.51%, and 92.39 ± 2.82%, respectively. Notably, the analysis showed no associated difference in morphometric measurements between the manual and automatic segmentation at the L5/S1 level.LIMITATIONS : Semantic segmentation of multiple spinal structures (such as VBSs, IVDs, blood vessels, muscles, and ligaments) was simultaneously not integrated into the deep-learning method in this study. In addition, large clinical experiments are needed to evaluate the clinical efficacy of the model.CONCLUSION : The 3D-UNet model developed in this study based on deep learning can effectively and simultaneously segment VBSs and IVDs at L5/S1 level formMR images, thereby enabling rapid and accurate 3D reconstruction of LIVF models. The method can be used to segment VBSs and IVDs of spinal structures on MR images within near-human expert performance; therefore, it is reliable for reconstructing LIVF for L5/S1 LTRFA.Liu Zheng, Su Zhihai, Wang Min, Chen Tao, Cui Zhifei, Chen Xiaojun, Li Shaolin, Feng Qianjin, Pang Shumao, Lu Hai2022-Jan** 3D reconstruction \r, 3D-UNet model, MRI, automatic segmentation, intervertebral discs, lumbosacral intervertebral foramen, manual segmentation, vertebral bone structures, Deep learning**

Internal Medicine

Public Health

General

General

General

Public Health

Public Health

General

General

General

General

General

Cardiology

Surgery

Public Health

General

General

General

General

General

Surgery

Ophthalmology

General

Pathology

Surgery

General

Pathology

General

Pathology

General

General

Public Health

Public Health

General

General

General

General

General

General

General

General

General

General

General

General

Public Health

General

General

General

Ophthalmology

Cardiology

Oncology

General

Public Health

General

Public Health

Surgery

Dermatology

General

General

General

General

Oncology

General

General

General

General

Public Health

Public Health

General

General

General

General

General

## Value of artificial intelligence with novel tumor tracking technology in the diagnosis of gastric submucosal tumors by contrast-enhanced harmonic endoscopic ultrasonography.

#### In Journal of gastroenterology and hepatology ; h5-index 51.0 ** : BACKGROUND AND AIMS Contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH-EUS.METHODS : This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH-EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH-EUS videos. CH-EUS was evaluated by AI using deep learning involving a residual neural network and leave-one-out cross-validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers.RESULTS : Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ -coefficient between the two reviewers was 0.713.CONCLUSIONS : The diagnostic ability of CH-EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.Tanaka Hidekazu, Kamata Ken, Ishihara Rika, Handa Hisashi, Otsuka Yasuo, Yoshida Akihiro, Yoshikawa Tomoe, Ishikawa Rei, Okamoto Ayana, Yamazaki Tomohiro, Nakai Atsushi, Omoto Shunsuke, Minaga Kosuke, Yamao Kentaro, Takenaka Mamoru, Watanabe Tomohiro, Nishida Naoshi, Kudo Masatoshi2022-Jan-18artificial intelligences, contrast-enhanced harmonic endoscopic ultrasonography, endoscopic ultrasonography, gastrointestinal stromal tumor, neural network, submucosal tumor

Internal Medicine

General

Surgery

General

General

General

Oncology

Oncology

General

General

General

General

General

General

General

General

General

General

General

Ophthalmology

Oncology

General

General

Dermatology

General

General

General

General

General

General

Oncology

General

Public Health

General

General

General

General

General

Public Health

Public Health

General

General

General

General

Ophthalmology

General

Public Health

General

General

Public Health

General

## Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

#### In BMC neuroscience Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainNet-GA CNN showed an accuracy of 83.13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainNet-GA CNN can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainNet-GA CNN in the diagnosis of schizophrenia.Oh Kang-Han, Oh Il-Seok, Tsogt Uyanga, Shen Jie, Kim Woo-Sung, Liu Congcong, Kang Nam-In, Lee Keon-Hak, Sui Jing, Kim Sung-Wan, Chung Young-Chul2022-Jan-17Brain network, Convolutional neural network, Functional connectome, Global covariance pooling, Schizophrenia, Self-attention mechanism

Internal Medicine

General

General

General

General

Dermatology

General

Dermatology

General

General

General

General

## Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application.

#### In Yonsei medical journal PURPOSE : Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain.MATERIALS AND METHODS : The same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout.RESULTS : The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09; p<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26; p<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (p<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups.CONCLUSION : The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.Park Jinyoung, Chung Seok Young, Park Jung Hyun2022-JanCoaching, exercise, machine learning, mobile application, motion, neural network

Internal Medicine

General

General

General

Ophthalmology

General

General

General

General

General

General

General

Pathology

General

General

General

General

General

General

General

General

Oncology

General

General

General

General

General

General

## A data-driven ultrasound approach discriminates pathological high grade prostate cancer.

#### In Scientific reports ; h5-index 158.0 Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.Akatsuka Jun, Numata Yasushi, Morikawa Hiromu, Sekine Tetsuro, Kayama Shigenori, Mikami Hikaru, Yanagi Masato, Endo Yuki, Takeda Hayato, Toyama Yuka, Yamaguchi Ruri, Kimura Go, Kondo Yukihiro, Yamamoto Yoichiro2022-Jan-17

Internal Medicine

General

Pathology

General

General

General

General

General

General

General

Surgery

General

General

Public Health

## Molecular docking, molecular dynamics simulation and MM-GBSA studies of the activity of glycyrrhizin relevant substructures on SARS-CoV-2 RNA-dependent-RNA polymerase.

#### In Journal of biomolecular structure & dynamics SARS-CoV-2 is the causative agent of Coronavirus Disease (COVID-19), which is a life-threatening disease. The World Health Organization has classified COVID-19 as a severe worldwide public health pandemic due to its high death rate, quick transmission, and lack of medicines. To counteract the recurrence of the severe acute respiratory syndrome, active antiviral medications are urgently required. Glycyrrhizin was documented with activity on different viral proteins, including SARS-CoV-2; in this study, the activity of glycyrrhizin and its substructures (604 molecules) were screened on SARS-CoV-2 RNA-dependent-RNA polymerase using molecular docking, molecular dynamic (MD) simulation, and MM/GBSA. Sixteen molecules exhibited docking energy higher than -7 kcal/mol; four compounds (10772603, 101088272, 154730753 and glycyrrhizin) showed the highest binding energy, and good stability during MD simulation. The glycyrrhizin compound exhibited favorable docking energy (-7.9 kcal/mol), and it was the most stable complex during MD simulation. The predicted binding free energy of the glycyrrhizin complex was -57 ± 8 kcal/mol. These findings suggest that this molecule, after more validation, could become a good candidate for developing and manufacturing an anti-SARS-CoV-2 medication.Communicated by Ramaswamy H. Sarma.Zamzami Mazin A2022-Jan-17Coronavirus, RdRp, SARS-CoV-2, antiviral agent, molecular docking; natural compound

Internal Medicine

General

General

General

Public Health

Oncology

General

General

General

Surgery

General

Surgery

Pathology

Oncology

General

General

General

General

Surgery

Surgery

General

General

General