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2 2021

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Artificial Intelligence and Machine Learning in Emergency Medicine

Tang Kenneth Jian Wei, Ang Candice Ke En, Constantinides Theodoros, Rajinikanth V., Acharya U. Rajendra, Cheong Kang Hao

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

156--172

The advent of Artificial Intelligence (AI) has resulted in development of novel applications in a multitude of fields, such as in Medicine, to aid medical professionals in clinical diagnosis. Specifically, the field of Emergency Medicine has been of immense interest to researchers, with vast untapped potential for AI solutions to improve operational efficiencies and quality of healthcare. Aside from primary healthcare facilities, the Emergency Department serves as the first line of contact to patients, who often present with varying and undifferentiated symptoms. Several challenges faced by clinicians and patients alike, such as waiting times and diagnostic dilemmas, present opportunities for application of AI solutions. In this paper, we aim to summarise the applications of AI in the field of Emergency Medicine by reviewing recent developments in Emergency Department operations and in the clinical management of patients.

An automated skin melanoma detection system with melanoma-index based on entropy features

Cheong Kang Hao, Tang Kenneth Jian Wei, Zhao Xinxing, Koh Joel En Wei, Faust Oliver, Gururajan Raj, Ciaccio Edward J., Rajinikanth V., Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

997--1012

Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student’s t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given.