Article
Chemistry, Multidisciplinary
Sinan Alkassar, Mohammed A. M. Abdullah, Bilal A. Jebur, Ghassan H. Abdul-Majeed, Bo Wei, Wai Lok Woo
Summary: The paper introduces a new approach for diagnosing pneumonia, utilizing lightweight feature extraction and adaptive weight setup with Adaboost ensemble learning, achieving an accuracy of 99.6% on the Kaggle dataset.
APPLIED SCIENCES-BASEL
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yu Meng, Jingru Ruan, Bailin Yang, Yang Gao, Jianqiu Jin, Fangfang Dong, Hongli Ji, Linyang He, Guohua Cheng, Xiangyang Gong
Summary: This study developed and evaluated the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. The results showed that the automated system provided high performance in measuring quantitative indices and assessing image quality, and the deep learning model had high accuracy in predicting the quantitative indices. The study demonstrated the importance of automated assessment in improving the accuracy and efficiency of analyzing chest radiographs.
EUROPEAN RADIOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Sofia C. Pereira, Joana Rocha, Aurelio Campilho, Pedro Sousa, Ana Maria Mendonca
Summary: A lightweight multi-scale classification framework is proposed for analyzing medical detections in chest X-ray images. The framework leverages features of different granularities to improve classification accuracy, and it has fewer parameters.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Engineering, Biomedical
Swarup Kr Ghosh, Anupam Ghosh
Summary: This study proposes a modified residual network based enhancement scheme for visual clarification and classification of COVID-19 pneumonia from CXR images under a deep learning framework. The proposed model achieves high classification accuracy in binary and multi-class detection of COVID-19. The approach involves generating residual images through residual convolutional neural network, constructing modules with patches and residual images, and utilizing 'multi-term loss' and 'softmax' classifier in a simple CNN model for automatic detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Health Care Sciences & Services
Jordan Z. T. Sim, Yong-Han Ting, Yuan Tang, Yangqin Feng, Xiaofeng Lei, Xiaohong Wang, Wen-Xiang Chen, Su Huang, Sum-Thai Wong, Zhongkang Lu, Yingnan Cui, Soo-Kng Teo, Xin-Xing Xu, Wei-Min Huang, Cher-Heng Tan
Summary: This study developed an AI model that accurately detects pneumonia in COVID-19 suspects and assessed its performance in a clinical setting, finding that the model can expedite reporting without significant impact on diagnostic performance.
Article
Computer Science, Interdisciplinary Applications
Bowen Wang, Toshihiro Takeda, Kento Sugimoto, Jiahao Zhang, Shoya Wada, Shozo Konishi, Shirou Manabe, Katsuki Okada, Yasushi Matsumura
Summary: By utilizing positional information extracted from CXR reports, this study aimed to generate bounding boxes with disease lesions on CXR images. Through semantic segmentation and classification models, object detection on the generated attention bounding boxes improved the precision of nodule detection.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Information Systems
Gurmail Singh, Kin-Choong Yow
Summary: The study introduced an interpretable deep learning model Gen-ProtoPNet, which achieves accuracy comparable to the best performing non-interpretable models. The model utilizes a generalized version of the distance function L2 and prototypes of different spatial dimensions for image classification.
Article
Computer Science, Artificial Intelligence
Xiyue Wang, Sen Yang, Jun Lan, Yuqi Fang, Jianhui He, Minghui Wang, Jing Zhang, Xiao Han
Summary: This study proposed a two-stage deep learning method for pneumothorax segmentation, which achieved good results in pneumothorax diagnosis through ensemble of multiple models and multitask learning strategy.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
R. Karthik, R. Menaka, M. Hariharan
Summary: Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and strengthens coronavirus testing methods. A custom CNN architecture has been proposed in this research to learn unique convolutional filter patterns for each kind of pneumonia, showing significant potential in augmenting current testing methods for COVID-19.
APPLIED SOFT COMPUTING
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Marie-Pierre Debray, Helena Tarabay, Lisa Males, Nisrine Chalhoub, Elyas Mahdjoub, Thomas Pavlovsky, Benoit Visseaux, Donia Bouzid, Raphael Borie, Catherine Wackenheim, Bruno Crestani, Christophe Rioux, Loukbi Saker, Christophe Choquet, Jimmy Mullaert, Antoine Khalil
Summary: The study assessed the interobserver agreement and clinical significance of chest CT reporting in patients suspected of COVID-19, showing good agreement between observers. Among patients suspected of COVID-19, CT categorized as evocative is highly predictive of COVID-19, while the predictive value decreases between the categories compatible and not evocative.
EUROPEAN RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ecem Sogancioglu, Keelin Murphy, Ernst Th Scholten, Luuk H. Boulogne, Mathias Prokop, Bram van Ginneken
Summary: This study investigates the performance of deep-learning approaches for automated measurement of total lung volume from chest radiographs. The results show that the proposed method accurately measures total lung volume and is highly correlated with the reference standard.
Article
Biology
Narathip Reamaroon, Michael W. Sjoding, Jonathan Gryak, Brian D. Athey, Kayvan Najarian, Harm Derksen
Summary: Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury with high mortality worldwide. This study demonstrates the potential capability of artificial intelligence and machine learning approaches in quantitatively evaluating chest x-rays for detecting the presence of ARDS.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Review
Computer Science, Information Systems
Tahira Iqbal, Arslan Shaukat, Muhammad Usman Akram, Zartasha Mustansar, Aimal Khan
Summary: Chest radiographs are the most important diagnostic tool for thoracic pathologies, with promising results being found in automating medicine through Artificial Intelligence techniques. Studies show pneumothorax is more common in men, and deep learning models have achieved good results in classification and localization of pneumothorax.
Article
Computer Science, Information Systems
Hassaan Malik, Tayyaba Anees, Muizzud Din, Ahmad Naeem
Summary: A deep learning model based on a convolutional neural network has been developed and tested to automatically classify chest X-ray images of COVID-19 and other chest diseases. The proposed model shows high accuracy in diagnosing chest diseases and outperforms other pre-trained models.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Shadi A. Aljawarneh, Romesaa Al-Quraan
Summary: The aim of this study was to develop models to evaluate large X-ray images of the chest and determine whether the images show signs of pneumonia. The enhanced CNN model showed the highest accuracy for pneumonia detection.
Article
Clinical Neurology
Manish Ramesh Patil, Imran Rizvi, Ravindra Kumar Garg, Hardeep Singh Malhotra, Neeraj Kumar, Ravi Uniyal, Shweta Pandey, Rajesh Verma, Praveen Kumar Sharma
Summary: Approximately 15.3% of patients with tuberculous meningitis needed re-hospitalization, with paradoxical neurological deterioration being the main reason. Re-hospitalization had an adverse impact on prognosis.
ACTA NEUROLOGICA BELGICA
(2023)
Article
Dermatology
Himanshu Dandu, Manish Kumar, Hardeep Singh Malhotra, Naveen Kumar, Neeraj Kumar, Prashant Gupta, Bipin Puri, Geeta Yadav
Summary: This study investigated the characteristics of T-cells in mucormycosis patients and found that T-cell immune dysfunction is more severe in non-COVID patients, indicating a continuous activation followed by extreme exhaustion state.
Article
Public, Environmental & Occupational Health
Kinzang Wangda, Neeraj Kumar, Ravindra Kumar Garg, Hardeep Singh Malhotra, Imran Rizvi, Ravi Uniyal, Shweta Pandey, Kiran Preet Malhotra, Rajesh Verma, Praveen Kumar Sharma, Anit Parihar, Amita Jain
Summary: Disseminated neurocysticercosis was found in patients with multiple neurocysticercosis brain lesions. Treatment with albendazole resulted in a significant reduction in neurocysticercosis lesions throughout the body.
TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE
(2023)
Article
Virology
Vijayaragavan Vijayavarman, Hardeep S. S. Malhotra, Imran Rizvi, Neeraj Kumar, Shweta Pandey, Mili Jain, Wahid Ali, Vinay Suresh, Ravindra K. K. Garg, Amita Jain, Rajesh Verma, Praveen Sharma, Ravi Uniyal
Summary: Subacute sclerosing panencephalitis (SSPE) is a chronic progressive neurological condition caused by a defective measles virus. This study aimed to analyze the pattern of immune dysregulation in SSPE patients and assess the correlation between measured immunological variables and disability/death at 6 months.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Neha Sengar, Rakesh Chandra Joshi, Malay Kishore Dutta, Radim Burget
Summary: This paper presents an automated deep learning-based framework for diagnosing multiple eye diseases using colour fundus images. The EyeDeep-Net, a multi-layer neural network, is developed to extract relevant features from the input dataset and make predictive diagnostic decisions. The proposed model shows superior performance compared to baseline models in terms of classification and disease identification through digital fundus images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Clinical Neurology
Ravindra Kumar Garg, Vimal Paliwal, Imran Rizvi, Shweta Pandey, Ravi Uniyal, Smriti Agrawal, Richa Khanna
Summary: This systematic review focuses on the maternal and foetal outcomes among pregnant women with subacute sclerosing panencephalitis (SSPE). The study found that SSPE in pregnancy usually has devastating consequences, but the majority of foetuses can survive. SSPE is often missed as it mimics eclampsia. Universal early childhood measles vaccination is the only way to fight this menace.
NEUROLOGICAL SCIENCES
(2023)
Article
Chemistry, Analytical
Jesus Galvan-Ruiz, Carlos M. Travieso-Gonzalez, Alejandro Pinan-Roescher, Jesus B. Alonso-Hernandez
Summary: According to WHO, a significant percentage of the global population faces difficulty in oral communication due to hearing disorders. This article discusses the importance of developing tools to aid in daily communication for these individuals. The research focuses on transcribing Spanish Sign Language (SSL) using a Leap Motion volumetric sensor capable of recognizing hand movements in 3D. By collaborating with a hearing-impaired subject and recording 176 dynamic words, the research achieves an accuracy of 95.17% in predicting input through the use of Dynamic Time Warping (DTW).
Article
Computer Science, Information Systems
Aayushi Chaudhari, Chintan Bhatt, Achyut Krishna, Carlos M. Travieso-Gonzalez
Summary: Emotion recognition is a challenging research field that involves various cognitive-emotional cues such as language, expressions, and speech. By using video input, a large amount of data can be obtained for analyzing human emotions. In this research, pretrained self-supervised learning models are used to extract features from text, audio, and visual data modalities. The fusion of these features and representations is the main challenge in multimodal emotion classification research. To address the high dimensionality of self-supervised learning characteristics, a unique fusion method based on Transformer and attention is proposed, achieving an accuracy of 86.40% for multimodal emotion classification.
Article
Engineering, Civil
Rakesh Chandra Joshi, Dongryeol Ryu, Patrick N. J. Lane, Gary J. Sheridan
Summary: This study integrated remotely sensed plant response, meteorological forcing, and landscape attributes into a machine learning model to forecast summer soil moisture over forested landscapes. The results showed promising potential for applications in forest hydrology and bushfire risk planning.
JOURNAL OF HYDROLOGY
(2023)
Article
Clinical Neurology
Ravi Uniyal, Ravindra Kumar Garg, Hardeep Singh Malhotra, Neeraj Kumar, Shweta Pandey, Imran Rizvi, Amita Jain, Nidhi Tejan, Rupesh Singh Kirar
Summary: We present a case of a 37-year-old man with visual loss and visual hallucinations. He also had seizures. Examination showed no perception of light rays and disc oedema with peripapillary small haemorrhages. MRI revealed abnormalities in white and gray matter, and CSF examination showed the presence of anti-measles IgG antibodies. This case highlights the importance of considering SSPE in the differential diagnosis of acute vision loss in measles-endemic regions.
NEURO-OPHTHALMOLOGY
(2023)
Article
Public, Environmental & Occupational Health
Kripashankar Nayak, Jyoti Mehra, Naresh P. Singh, Ankita Sharma, Pankaj K. Jain, Kiran Krishnappa
Summary: This study aimed to determine the incidence and various determinants of low birth weight among babies delivered at rural tertiary care hospitals in central Uttar Pradesh. The study found that 23% of babies had low birth weight out of a total of 7615 deliveries. There was a significant association between birth weight of babies and factors such as maternal age, parity, gestation period, and presence of complications during the antenatal period.
INDIAN JOURNAL OF COMMUNITY HEALTH
(2023)
Article
Mathematics
Sergio Celada-Bernal, Guillermo Perez-Acosta, Carlos M. Travieso-Gonzalez, Jose Blanco-Lopez, Luciano Santana-Cabrera
Summary: This paper aims to predict the medical test values of COVID-19 patients in the ICU, providing healthcare professionals with additional tools and information to combat COVID-19 by retrieving the missing medical test values.
Review
Infectious Diseases
Ravindra Kumar Garg, Shweta Pandey, Imran Rizvi, Ravi Uniyal, Praveen Kumar Sharma, Neeraj Kumar, Vimal Paliwal
Summary: This systematic review examines the seizure characteristics, specifically seizure semiology, in patients with SSPE, as well as the impact of seizures on the course of the disease. Multiple databases were used to gather information, and the quality of the data was assessed using the Joanna Briggs Institute Critical Appraisal tool. The review identified common seizure types and difficulties in controlling seizures in these patients.
CURRENT TROPICAL MEDICINE REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Shivangi Surati, Himani Trivedi, Bela Shrimali, Chintan Bhatt, Carlos M. Travieso-Gonzalez
Summary: This research aims to classify Monkeypox, Chickenpox, and Measles using trained standalone DL models and a SENet attention model, improving the accuracy of their diagnosis and classification. The proposed method achieves considerable success in accuracy, precision, recall, and F1-score, enhancing the overall performance of classification for Monkeypox.
MULTIMODAL TECHNOLOGIES AND INTERACTION
(2023)
Meeting Abstract
Hematology
Geeta Yadav, Himanshu Dandu, Hardeep Singh Malhotra, Shailendra Prasad Verma, Wahid Ali
INTERNATIONAL JOURNAL OF LABORATORY HEMATOLOGY
(2023)