Wasif Akbar¹, Adbul Mannan², Qaisar Shaheen^3,*, Mohammad Hijji⁴, Muhammad Anwar⁵, Muhammad Ayaz⁶

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6269-6286, 2023, DOI:10.32604/cmc.2023.036141

Abstract Machine Learning (ML) has changed clinical diagnostic procedures drastically. Especially in Cardiovascular Diseases (CVD), the use of ML is indispensable to reducing human errors. Enormous studies focused on disease prediction but depending on multiple parameters, further investigations are required to upgrade the clinical procedures. Multi-layered implementation of ML also called Deep Learning (DL) has unfolded new horizons in the field of clinical diagnostics. DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets. This paper proposed a novel method that deals with the issue of less data dimensionality. Inspired by the regression analysis, the… More >

Qaisar Abbas¹, Abdul Rauf Baig^1,*, Ayyaz Hussain²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1483-1499, 2023, DOI:10.32604/cmc.2023.036366

Abstract The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the… More >

B. Karthikeyan^1,*, K. Nithya², Ahmed Alkhayyat³, Yousif Kerrar Yousif⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2299-2313, 2023, DOI:10.32604/iasc.2023.032585

Abstract In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the… More >

Hanan Abdullah Mengash¹, Lubna A. Alharbi², Saud S. Alotaibi³, Sarab AlMuhaideb⁴, Nadhem Nemri⁵, Mrim M. Alnfiai⁶, Radwa Marzouk¹, Ahmed S. Salama⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4483-4500, 2023, DOI:10.32604/cmc.2023.032588

Abstract In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed… More >

Manjur Kolhar^*, Mohammed Misfer

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 529-542, 2023, DOI:10.32604/iasc.2023.030098

Abstract Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively. Further improvement, however, is required when both of these technologies are combined to reap maximum benefits. A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused. The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process. Treatment of heart failure may be improved… More >

Benjamin Zielonka^1,2,*, David M. Harrild^1,2, Sunil J. Ghelani^1,2, Eleni G. Elia^1,2, Christopher W. Baird^3,4, Andrew J. Powell^1,2, Rahul H. Rathod^1,2

Congenital Heart Disease, Vol.17, No.5, pp. 591-603, 2022, DOI:10.32604/chd.2022.021509

Abstract Background: The right ventricle to pulmonary artery conduit (RVPAC) may impair right ventricular (RV) function in patients with functional single right ventricles. Modification of the RVPAC using a ring-reinforced end with dunked insertion into the RV through a limited ventriculotomy may reduce the impact on RV function. We compared RV segmental strain between patients with a traditional RVPAC and ring-reinforced RVPAC using feature tracking cardiovascular magnetic resonance (CMR) imaging. Methods: Patients with CMR examinations after Stage I operation with RVPAC between 2000 and 2018 were reviewed. Ventricular mass, volumes, late gadolinium enhancement (LGE), and peak radial and circumferential strain of… More >

S. Raghavendra¹, Vasudev Parvati², R. Manjula³, Ashok Kumar Nanda⁴, Ruby Singh⁵, D. Lakshmi⁶, S. Velmurugan^7,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1353-1368, 2023, DOI:10.32604/iasc.2023.027977

Abstract In contemporary medicine, cardiovascular disease is a major public health concern. Cardiovascular diseases are one of the leading causes of death worldwide. They are classified as vascular, ischemic, or hypertensive. Clinical information contained in patients’ Electronic Health Records (EHR) enables clinicians to identify and monitor heart illness. Heart failure rates have risen dramatically in recent years as a result of changes in modern lifestyles. Heart diseases are becoming more prevalent in today’s medical setting. Each year, a substantial number of people die as a result of cardiac pain. The primary cause of these deaths is the improper use of pharmaceuticals… More >

Hugues Lucron^1,*, Alban-Elouen Baruteau^2,3, Caroline Ovaert⁴, Ali Houeijeh⁵, Mélanie Brard¹, Patrice Guerin², François Bourlon⁶, Claire Dauphin⁷, Saskia Tuttle¹, Maha Tagorti³, Rishika Banydeen⁸, François Godart⁵

Congenital Heart Disease, Vol.17, No.4, pp. 421-436, 2022, DOI:10.32604/chd.2022.020835

Abstract Objectives: We aim to describe the efficacy, safety, and characteristics of the Amplatzer Vascular Plug (AVP) II and IV “off-label” use for multiple cardiovascular occlusions in children under 10 years. Methods: Observational retrospective multicenter (2007–2020, 6 centers) review of paediatric procedures using AVP II or IV. Results: A total of 125 children (49.6% aged ≤ 1 year, 147 lesions) underwent 136 successive procedures (success rate: 98.5%) using 169 devices (109 AVP IV, 60 AVP II). The mean device diameter was 7.7 ± 3.2 mm (4–20 mm). The median AVP size to vessel diameter ratio was 1.3 (0–2). The median age and weight… More > Graphic Abstract

Rachel Gardner¹, Emily Durbak¹, Rachael Baird², Katherine Singh², Jeff Chapa², David Majdalany^3,*

Congenital Heart Disease, Vol.17, No.2, pp. 147-160, 2022, DOI:10.32604/chd.2022.017366

Abstract Background: There is limited literature written on the course and outcomes for pregnant mothers with Shone complex. Methods: We describe a case series of five pregnancies in four women with Shone complex within a multidisciplinary cardio-obstetrics clinic from 2016–2018. Results: Maternal age ranged from 21–39 years. Three patients had preserved left ventricular function while one had moderately decreased function. Gestational age at presentation ranged from 6–15 weeks. There were three successful pregnancies (mean gestational age = 37 weeks, range 35–39 weeks) with one patient accounting for two unsuccessful pregnancies. All infants were delivered via Cesarean section. One infant required a… More >

Fuad Ali Mohammed Al-Yarimi^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >