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General General

Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.

In Journal of healthcare engineering

The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR. In consequence, deep learning-based COVID-19 detection from chest CT images is gaining momentum. Furthermore, visual analysis of data has enhanced the opportunities of maximizing the prediction performance in this big data and deep learning realm. In this article, we have proposed two separate deformable deep networks converting from the conventional convolutional neural network (CNN) and the state-of-the-art ResNet-50, to detect COVID-19 cases from chest CT images. The impact of the deformable concept has been observed through performance comparative analysis among the designed deformable and normal models, and it is found that the deformable models show better prediction results than their normal form. Furthermore, the proposed deformable ResNet-50 model shows better performance than the proposed deformable CNN model. The gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions' localization effort at the final convolutional layer and has been found excellent. Total 2481 chest CT images have been used to evaluate the performance of the proposed models with a train-valid-test data splitting ratio of 80 : 10 : 10 in random fashion. The proposed deformable ResNet-50 model achieved training accuracy of 99.5% and test accuracy of 97.6% with specificity of 98.5% and sensitivity of 96.5% which are satisfactory compared with related works. The comprehensive discussion demonstrates that the proposed deformable ResNet-50 model-based COVID-19 detection technique can be useful for clinical applications.

Foysal Md, Hossain A B M Aowlad, Yassine Abdulsalam, Hossain M Shamim

2023

General General

A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors.

In Applied intelligence (Dordrecht, Netherlands)

This paper proposes a method to assist patients in finding the most appropriate doctor for online medical consultation. To do that, it constructs an online doctor selection decision-making method that considers the correlation attributes, in which the measure of attribute correlation is derived from the history real decision data. To combine public and personal preference with correlated attributes, it proposes a Choquet integral based comprehensive online doctor ranking method. In detail, a two stage classification model based on BERT (Bidirectional Encoder Representations from Transformers) is used to extract service features from unstructured text reviews. Then, 2-additive fuzzy measure is adopted to represent the patient public group aggregated attribute preference. Next, a novel optimization model is proposed to combine the public preference and personal preference. Finally, a case study of dxy.com is carried out to illustrate the procedure of the method. The comparison result between proposed method and other traditional MADM (multi-attribute decision-making) methods prove its rationality.

Wu Jian, Zhang Guangyin, Xing Yumei, Liu Yujia, Zhang Zhen, Dong Yucheng, Herrera-Viedma Enrique

2023-Feb-20

2-additive fuzzy measure, Choquet integral, Doctor selection, Multi-attribute decision-making, Online reviews, Sentiment analysis

General General

Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network.

In Environmental development

The need for stability in the economy for world development has been a challenge due to the COVID-19 pandemic. In addition, the increase of natural disasters and their aftermath have been increasing causing damages to infrastructure, the economy, livelihood, and lives in general. This study aimed to determine factors affecting the intention to donate for victims of Typhoon Odette, a recent super typhoon that hit the Philippines leading to affect 38 out of 81 provinces of the most natural disaster-prone countries. Determining the most significant factor affecting the intention to donate may help in increasing the engagement of donations among other people to help establish a more stable economy to heighten world development. With the use of deep learning neural network, a 97.12% accuracy was obtained for the classification model. It could be deduced that when donors understand and perceive both severity and vulnerability to be massive and highly damaging, then a more positive intention to donate to victims of typhoons will be observed. In addition, the influence of other people, the holiday season when the typhoon happened, and the media as a platform have greatly contributed to heightening the intention to donate and control over the donor's behavior. The findings of this study could be applied and utilized by government agencies and donation platforms to help engage and promote communication among donors. Moreover, the framework and methodology considered in this study may be extended to evaluate intention, natural disasters, and behavioral studies worldwide.

German Josephine D, Ong Ardvin Kester S, Redi Anak Agung Ngurah Perwira, Prasetyo Yogi Tri, Robas Kirstien Paola E, Nadlifatin Reny, Chuenyindee Thanatorn

2023-Mar

Deep learning neural network, Donation, Natural disaster, Typhoon odette, Typhoon victims

General General

Emerging trends in point-of-care biosensing strategies for molecular architectures and antibodies of SARS-CoV-2.

In Biosensors & bioelectronics: X

COVID-19, a highly contagious viral infection caused by the occurrence of severe acute respiratory syndrome coronavirus (SARS-CoV-2), has turned out to be a viral pandemic then ravaged many countries worldwide. In the recent years, point-of-care (POC) biosensors combined with state-of-the-art bioreceptors, and transducing systems enabled the development of novel diagnostic tools for rapid and reliable detection of biomarkers associated with SARS-CoV-2. The present review thoroughly summarises and discusses various biosensing strategies developed for probing SARS-CoV-2 molecular architectures (viral genome, S Protein, M protein, E protein, N protein and non-structural proteins) and antibodies as a potential diagnostic tool for COVID-19. This review discusses the various structural components of SARS-CoV-2, their binding regions and the bioreceptors used for recognizing the structural components. The various types of clinical specimens investigated for rapid and POC detection of SARS-CoV-2 is also highlighted. The importance of nanotechnology and artificial intelligence (AI) approaches in improving the biosensor performance for real-time and reagent-free monitoring the biomarkers of SARS-CoV-2 is also summarized. This review also encompasses existing practical challenges and prospects for developing new POC biosensors for clinical monitoring of COVID-19.

Karuppaiah Gopi, Vashist Arti, Nair Madhavan, Veerapandian Murugan, Manickam Pandiaraj

2023-May

Biomarkers, COVID-19, Electrochemical and optical biosensors, Infectious diseases, Nanobiosensors

General General

Impact of word embedding models on text analytics in deep learning environment: a review.

In Artificial intelligence review

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

Asudani Deepak Suresh, Nagwani Naresh Kumar, Singh Pradeep

2023-Feb-22

Deep learning, Natural language processing, Text analytics, Word embedding

Public Health Public Health

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

In Frontiers in public health

INTRODUCTION : The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.

METHODS : In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.

RESULTS : The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.

DISCUSSION : In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

Li Hao, Tao Xiang, Liang Tuo, Jiang Jie, Zhu Jichong, Wu Shaofeng, Chen Liyi, Zhang Zide, Zhou Chenxing, Sun Xuhua, Huang Shengsheng, Chen Jiarui, Chen Tianyou, Ye Zhen, Chen Wuhua, Guo Hao, Yao Yuanlin, Liao Shian, Yu Chaojie, Fan Binguang, Liu Yihong, Lu Chunai, Hu Junnan, Xie Qinghong, Wei Xiao, Fang Cairen, Liu Huijiang, Huang Chengqian, Pan Shixin, Zhan Xinli, Liu Chong

2023

ankylosing spondylitis, artificial intelligence, deep learning, machine learning, pelvic radiograph