Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

This paper focuses on a healthcare solution capable of converting echocardiography videos into text, and vice versa, for transmission between urban and rural areas. People living in rural areas often encounter restrictions on essential healthcare services because of large distances. Telemedicine provides a possible answer to this problem. Several wireless technologies have been used in telemedicine systems and have widen the availability of medical services. However, wireless connectivity for mobile devices remains low in rural areas, especially Malaysia. The transmission of digital media requires high bandwidth wireless connections due to large file sizes. Without it, transmission delays may prevent effective patient care. Furthermore, in areas lacking internet connectivity, these services may cease to function. Preliminary experiments show that the proposed solution can achieve a file size reduction ranging from 66% to 96% when converting video to text.

The demand for aquaculture tools have been increasing due to the rising needs for fish. Several factors show the potential requirements for software solutions. For instance, the continual market value growth of fish, government focus on innovative technologies and research on increasing fish production. The Aquatic Tool Kit aims to calculate the total number of larval and juvenile fish through image processing techniques. Furthermore, it intends to address existing issues during fish counting which has lead to poor accuracy rates and high margins of error. Edge detection and basic morphology tools are utilized to segment and identify fish larvae from acquired images. A preliminary experiment shows a potentially high accuracy rate when detecting larvae and ants.

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Introduction The combination of medical knowledge, experience and AI algorithms have supported the advancement of patient care and the lowering of healthcare costs. Machine and deep learning methods enable the extraction of meaningful patterns that remain beyond human perception. Numerous computer-aided diagnosis and detection systems have been developed to assist in the assessment of stress echocardiograms. However, issues are encountered when facing imbalanced, limited, and unannotated datasets. Learning from imbalanced medical datasets impairs diagnostic accuracy due to classifier bias and overfitting. Furthermore, datasets comprising of all existing abnormal classes are impossible to obtain, hence supervised algorithms would fail to generate predictions for classes devoid of training samples. Moreover, reliance on prior knowledge in the form of expert annotation and anatomical region extraction impairs scalability, as these procedures are time-consuming, computationally expensive, and limited to specific tasks. Purpose We aimed to perform one-class classification and anomaly detection of stress echocardiograms using unsupervised deep learning techniques to discriminate between normal and abnormal videos as well as to localise wall motion abnormalities within individual frames. Methods Deep denoising spatio-temporal autoencoder networks were employed to learn visual and motion representations from multiple echocardiographic cross-sections and stress stages. Extracted middle layer features were modelled by one-class support vector machines to discriminate between regular and irregular echocardiograms despite the absence of abnormal training samples. Reconstruction errors allowed for direct visualisation and localisation of anomalous cardiac regions, without the need for annotated training data or segmentation of structures. Results 2D B-mode stress echocardiograms acquired from 36 patients were classified as normal or abnormal based on patient reports and served as the ground truth. Results revealed that learnt features extracted from spatio-temporal autoencoders trained solely on normal data can be utilised to classify abnormal echocardiograms with a high level of accuracy, sensitivity and specificity. In addition to that, as validated by an expert reader, spatio-temporal autoencoder reconstruction errors were capable of detecting and localising wall motion abnormalities in specific cardiac regions without prior knowledge of abnormal segments. Conclusions The trained model enables the classification and detection of spatio-temporal abnormalities in stress echocardiograms. Therefore, the proposed networks have the potential of assisting in the global and regional assessment of stress echocardiograms.