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

Cardiology Cardiology

Development and validation of a nomogram to predict mortality risk in patients with ischemic heart disease.

In Frontiers in cardiovascular medicine

BACKGROUND : Ischemic Heart Disease (IHD) is the leading cause of death from cardiovascular disease. Currently, most studies have focused on factors influencing IDH or mortality risk, while few predictive models have been used for mortality risk in IHD patients. In this study, we constructed an effective nomogram prediction model to predict the risk of death in IHD patients by machine learning.

METHODS : We conducted a retrospective study of 1,663 patients with IHD. The data were divided into training and validation sets in a 3:1 ratio. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen the variables to test the accuracy of the risk prediction model. Data from the training and validation sets were used to calculate receiver operating characteristic (ROC) curves, C-index, calibration plots, and dynamic component analysis (DCA), respectively.

RESULTS : Using LASSO regression, we selected six representative features, age, uric acid, serum total bilirubin, albumin, alkaline phosphatase, and left ventricular ejection fraction, from 31 variables to predict the risk of death at 1, 3, and 5 years in patients with IHD, and constructed the nomogram model. In the reliability of the validated model, the C-index at 1, 3, and 5 years was 0.705 (0.658-0.751), 0.705 (0.671-0.739), and 0.694 (0.656-0.733) for the training set, respectively; the C-index at 1, 3, and 5 years based on the validation set was 0.720 (0.654-0.786), 0.708 (0.650-0.765), and 0.683 (0.613-0.754), respectively. Both the calibration plot and the DCA curve are well-behaved.

CONCLUSION : Age, uric acid, total serum bilirubin, serum albumin, alkaline phosphatase, and left ventricular ejection fraction were significantly associated with the risk of death in patients with IHD. We constructed a simple nomogram model to predict the risk of death at 1, 3, and 5 years for patients with IHD. Clinicians can use this simple model to assess the prognosis of patients at the time of admission to make better clinical decisions in tertiary prevention of the disease.

Yang Long, Dong Xia, Abuduaini Baiheremujiang, Jiamali Nueraihemaiti, Seyiti Zulihuma, Shan Xue-Feng, Gao Xiao-Ming

2023

LASSO, ischemic heart disease, machine learning, mortality, nomogram

Radiology Radiology

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.

In Frontiers in cardiovascular medicine

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Baeßler Bettina, Götz Michael, Antoniades Charalambos, Heidenreich Julius F, Leiner Tim, Beer Meinrad

2023

artificial intelligence, cardiac computed tomography, clinical workflow, coronary computed tomography angiography, deep learning, machine learning, radiomics

General General

Design and Proofreading of the English-Chinese Computer-Aided Translation System by the Neural Network.

In Computational intelligence and neuroscience

At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.

Liu Yutong, Zhang Shile

2023

General General

A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer's Disease Using EEG Signal.

In Computational intelligence and neuroscience

Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer's disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.

Swarnalatha R

2023

General General

Computational Thinking Training and Deep Learning Evaluation Model Construction Based on Scratch Modular Programming Course.

In Computational intelligence and neuroscience

To improve the algorithmic dimension, critical thinking, and problem-solving ability of computational thinking (CT) in students' programming courses, first, a programming teaching model is constructed based on the scratch modular programming course. Secondly, the design process of the teaching model and the problem-solving model of visual programming are studied. Finally, a deep learning (DL) evaluation model is constructed, and the effectiveness of the designed teaching model is analyzed and evaluated. The T-test result of paired samples of CT is t = -2.08, P < 0.05. There are significant differences in the results of the two tests, and the designed teaching model can cause changes in students' CT abilities. The results reveal that the effectiveness of the teaching model based on scratch modular programming has been verified on the basis of experiments. The post-test values of the dimensions of algorithmic thinking, critical thinking, collaborative thinking, and problem-solving thinking are all higher than the pretest values, and there are individual differences. The P values are all less than 0.05, which testifies that the CT training of the designed teaching model has the algorithm dimension, critical thinking, collaborative thinking, and problem-solving ability of students' CT. The post-test values of cognitive load are all lower than the pretest values, indicating that the model has a certain positive effect on reducing cognitive load, and there is a significant difference between the pretest and post-test. In the dimension of creative thinking, the P value is 0.218, and there is no obvious difference in the dimensions of creativity and self-efficacy. It can be found from the DL evaluation that the average value of the DL knowledge and skills dimensions is greater than 3.5, and college students can reach a certain standard level in terms of knowledge and skills. The mean value of the process and method dimensions is about 3.1, and the mean value of the emotional attitudes and values is 2.77. The process and method, as well as emotional attitude and values, need to be strengthened. The DL level of college students is relatively low, and it is necessary to improve their DL level from the perspective of knowledge and skills, processes and methods, emotional attitudes and values. This research makes up for the shortcomings of traditional programming and design software to a certain extent. It has a certain reference value for researchers and teachers to carry out programming teaching practice.

Chen Xiaoli, Wang XiaoMing

2023

Surgery Surgery

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle.

In Frontiers in bioengineering and biotechnology

The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.

Wolff Christian, Steinheimer Patrick, Warmerdam Elke, Dahmen Tim, Slusallek Philipp, Schlinkmann Christian, Chen Fei, Orth Marcel, Pohlemann Tim, Ganse Bergita

2023

ageing, gait, ground reaction (forces), handgrip strengh, insoles, motion analysis, obesity, smart healthcare