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General General

Development of evaluation system for cerebral artery occlusion in emergency medical services: noninvasive measurement and utilization of pulse waves.

In Scientific reports ; h5-index 158.0

Rapid reperfusion therapy can reduce disability and death in patients with large vessel occlusion strokes (LVOS). It is crucial for emergency medical services to identify LVOS and transport patients directly to a comprehensive stroke center. Our ultimate goal is to develop a non-invasive, accurate, portable, inexpensive, and legally employable in vivo screening system for cerebral artery occlusion. As a first step towards this goal, we propose a method for detecting carotid artery occlusion using pulse wave measurements at the left and right carotid arteries, feature extraction from the pulse waves, and occlusion inference using these features. To meet all of these requirements, we use a piezoelectric sensor. We hypothesize that the difference in the left and right pulse waves caused by reflection is informative, as LVOS is typically caused by unilateral artery occlusion. Therefore, we extracted three features that only represented the physical effects of occlusion based on the difference. For inference, we considered that the logistic regression, a machine learning technique with no complex feature conversion, is a reasonable method for clarifying the contribution of each feature. We tested our hypothesis and conducted an experiment to evaluate the effectiveness and performance of the proposed method. The method achieved a diagnostic accuracy of 0.65, which is higher than the chance level of 0.43. The results indicate that the proposed method has potential for identifying carotid artery occlusions.

Shimada Takuma, Matsubara Kazumasa, Koyama Daisuke, Matsukawa Mami, Ohsaki Miho, Kobayashi Yasuyo, Saito Kozue, Yamagami Hiroshi

2023-Feb-27

General General

Pet dog facial expression recognition based on convolutional neural network and improved whale optimization algorithm.

In Scientific reports ; h5-index 158.0

Pet dogs are our good friends. Realizing the dog's emotions through the dog's facial expressions is beneficial to the harmonious coexistence between human beings and pet dogs. This paper describes a study on dog facial expression recognition using convolutional neural network (CNN), which is a representative algorithm model of deep learning. Parameter settings have a profound impact on the performance of a CNN model, improper parameter setting will make the model exposes several shortcomings, such as slow learning speed, easy to fall into local optimal solution, etc. In response to these shortcomings and improve the accuracy of recognition, a novel CNN model based on the improved whale optimization algorithm (IWOA) called IWOA-CNN is applied to complete this recognition task. Unlike human face recognition, a dedicated face detector in Dlib toolkit is utilized to recognize the facial region, and the captured facial images are augmented to build an expression dataset. The random dropout layer and L2 regularization are introduced into the network to reduce the number of transmission parameters of network and avoid over fitting. The IWOA optimizes the keep probability of the dropout layer, the parameter λ of L2 regularization and the dynamic learning rate of gradient descent optimizer. Carry out a comparative experiment of IWOA-CNN, Support Vector Machine, LeNet-5 and other classifiers for facial expression recognition, its results demonstrate that the IWOA-CNN has better recognition effect in facial expression recognition and also explain the efficiency of the swarm intelligence algorithm in dealing with model parameter optimization.

Mao Yan, Liu Yaqian

2023-Feb-27

Internal Medicine Internal Medicine

Current situation of telemedicine research for cardiovascular risk in Japan.

In Hypertension research : official journal of the Japanese Society of Hypertension

Hypertension continues to be a principal risk factor for the occurrence of cardiovascular disorders, stroke, and kidney diseases. Although more than 40 million subjects suffer from hypertension in Japan, its optimal control is achieved only a subpopulation of patients, highlighting the need for novel approaches to manage this disorder. Toward the better control of blood pressure, the Japanese Society of Hypertension has developed the Future Plan, in which the application of the state-of-art information and communication technology, including web-based resources, artificial intelligence, and big data analysis, is considered as one of the promising solutions. In fact, the rapid advance of digital health technologies, as well as ongoing coronavirus disease 2019 pandemic, has triggered the structural changes in the healthcare system globally, increasing demand for the remote delivery of the medical services. Nonetheless, it is not entirely clear what evidence exists that support the widespread use of telemedicine in Japan. Here, we summarize the current status of telemedicine research, particularly in the field of hypertension and other cardiovascular risk factors. We note that there have been very few interventional studies in Japan that clearly showed the superiority or noninferiority of telemedicine over standard care, and that the methods of online consultation considerably varied among studies. Clearly, more evidence is necessary for wide implementation of telemedicine in hypertensive patients in Japan, and also those with other cardiovascular risk factors.

Shibata Shigeru, Hoshide Satoshi

2023-Feb-27

Digital hypertension, High blood pressure, ICT, Online medical counseling, Telehealth

General General

Using machine learning with optical profilometry for GaN wafer screening.

In Scientific reports ; h5-index 158.0

To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques-including optical profilometry-produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available. For this research project, we fabricated over 6000 vertical PiN GaN diodes across 10 wafers. Using low resolution wafer scale optical profilometry data taken before fabrication, we successfully trained four different machine learning models. All models predict device pass and fail with 70-75% accuracy, and the wafer yield can be predicted within 15% error on the majority of wafers.

Gallagher James C, Mastro Michael A, Ebrish Mona A, Jacobs Alan G, Gunning Brendan P, Kaplar Robert J, Hobart Karl D, Anderson Travis J

2023-Feb-27

Cardiology Cardiology

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

In Scientific reports ; h5-index 158.0

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI < 0.02 or ≥ 0.02 µg/L using 12-lead ECGs. This was repeated with an alternative threshold of 1.0 µg/L and with single-lead ECG inputs. We also performed multiclass prediction for a set of serum troponin ranges. Finally, we tested the CNN in a cohort of patients selected for coronary angiography, including 3038 ECGs from 672 patients. Cohort patients were 49.0% female, 42.8% white, and 59.3% (19,283) never had a positive TnI value (≥ 0.02 µg/L). CNNs accurately predicted elevated TnI, both at a threshold of 0.02 µg/L (AUC = 0.783, 95% CI 0.780-0.786) and at a threshold of 1.0 µg/L (AUC = 0.802, 0.795-0.809). Models using single-lead ECG data achieved significantly lower accuracy, with AUCs ranging from 0.740 to 0.773 with variation by lead. Accuracy of the multi-class model was lower for intermediate TnI value-ranges. Our models performed similarly on the cohort of patients who underwent coronary angiography. Biomarker-defined myocardial injury can be predicted by CNNs from 12-lead and single-lead ECGs.

Chaudhari Gunvant R, Mayfield Jacob J, Barrios Joshua P, Abreau Sean, Avram Robert, Olgin Jeffrey E, Tison Geoffrey H

2023-Feb-27

General General

SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan.

In Scientific reports ; h5-index 158.0

Geological settings of the Karakoram Highway (KKH) increase the risk of natural disasters, threatening its regular operations. Predicting landslides along the KKH is challenging due to limitations in techniques, a challenging environment, and data availability issues. This study uses machine learning (ML) models and a landslide inventory to evaluate the relationship between landslide events and their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), and K Nearest Neighbor (KNN) models were used. A total of 303 landslide points were used to create an inventory, with 70% for training and 30% for testing. Susceptibility mapping used Fourteen landslide causative factors. The area under the curve (AUC) of a receiver operating characteristic (ROC) is employed to compare the accuracy of the models. The deformation of generated models in susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. The sensitive regions of the models showed elevated line-of-sight (LOS) deformation velocity. The XGBoost technique produces a superior Landslide Susceptibility map (LSM) for the region with the integration of SBAS-InSAR findings. This improved LSM offers predictive modeling for disaster mitigation and gives a theoretical direction for the regular management of KKH.

Kulsoom Isma, Hua Weihua, Hussain Sadaqat, Chen Qihao, Khan Garee, Shihao Dai

2023-Feb-27