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Digital Technology and Artificial Intelligence in Medicine and Dentistry

Submission Deadline: 30 May 2021 (closed)

Guest Editors

Dr. Dinesh Rokaya, Walailak University, Thailand
Dr. Pokpong Amornvit, Mahidol University, Thailand
Dr. Pisaisit Chaijareenont, Chiangmai University, Thailand
Dr. Sasiwimol Sanohkan, Prince of Songkla University, Thailand
Dr. Zohaib Khurshid, King Faisal University, KSA

Summary

There is rapid development in digital health and dentistry which utilizes digital technologies and artificial intelligence. This aim of this issue to provide insight into the recent advances in the field of digital health and dentistry. The topics in this Special Issue include in-vitro and clinical research utilizing digital equipment, such as intraoral and facial scanners; computer-aided design software; milling machines; and 3D printers.

Keywords

Digital, Technology, Computer-aided design, Computer-aided manufacturing, Dentistry, Medicine.

Published Papers

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Open Access

ARTICLE

Improved KNN Imputation for Missing Values in Gene Expression Data
Phimmarin Keerin, Tossapon Boongoen
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4009-4025, 2022, DOI:10.32604/cmc.2022.020261
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is… More >

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ARTICLE

AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies
Fahad Alotaibi, Muhammad Attique, Yaser Daanial Khan
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1039-1055, 2021, DOI:10.32604/cmc.2021.017297
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration. Though, the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process. Several anti-inflammatory prediction tools have been proposed in the recent past, yet, there is a space to induce enhancement in prediction performance in terms of precision and efficiency. An exceedingly accurate anti-inflammatory prediction model is proposed, named AntiFlamPred… More >

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ARTICLE

An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN
Shadi Nashwan
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 607-642, 2021, DOI:10.32604/cmc.2021.015597
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract The healthcare internet of things (IoT) system has dramatically reshaped this important industry sector. This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers. The goal is the remote monitoring of a patient’s physiological data by physicians. Moreover, this system can reduce the number and expenses of healthcare centers, make up for the shortage of healthcare centers in remote areas, enable consultation with expert physicians around the world, and increase the health awareness of communities. The major challenges that affect the rapid deployment and widespread acceptance of… More >

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ARTICLE

Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection
Oday Ali Hassen, Sarmad Omar Abter, Ansam A. Abdulhussein, Saad M. Darwish, Yasmine M. Ibrahim, Walaa Sheta
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution focuses on evaluating machine learning… More >

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ARTICLE

Identification of Antimicrobial Peptides Using Chou’s 5 Step Rule
Sharaf J. Malebary, Yaser Daanial Khan
CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2863-2881, 2021, DOI:10.32604/cmc.2021.015041
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract With the advancement in cellular biology, the use of antimicrobial peptides (AMPs) against many drug-resistant pathogens has increased. AMPs have a broad range of activity and can work as antibacterial, antifungal, antiviral, and sometimes even as anticancer peptides. The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments. Such experiments are both time-consuming and expensive. With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems. This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs. The proposed methodology uses machine… More >

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ARTICLE

Confocal 3D Optical Intraoral Scanners and Comparison of Image Capturing Accuracy
Pokpong Amornvit, Dinesh Rokaya, Chaimongkon Peampring, Sasiwimol Sanohkan
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 303-314, 2021, DOI:10.32604/cmc.2020.011943
（This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
Abstract Several capture techniques are used in intraoral optical scanners in the dental market, such as Triangulation (Cerec Omnicam, Dentsply Sirona), Activewave front sampling (3M ESPE) and confocal technology (iTero, Align). The accuracy of intraoral scanners is the most significant focal point for developers to research. This in-vitro study studied the accuracy of confocal scanners launched from 2015-2020 (Trios 3, Trios 4, iTero Element; 3Shape Trios A/S, Copenhagen, Denmark, and iTero Element2, and iTero Element5D; Align Technologies, San Jose, CA, USA). A 3D printing model modified from the American National Standard No. 132 was scanned five times each scanner. Both Trios3… More >

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Digital Technology and Artificial Intelligence in Medicine and Dentistry

Guest Editors

Summary

Keywords

Published Papers

View

1875

Download

1313

Cited by

1

View

2666

Download

1483

View

2745

Download

1262

Cited by

5

View

2196

Download

1258

View

2358

Download

1330

Cited by

3

View

3697

Download

3856

Cited by

9

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