Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Smartphone-Based Screening for Cardiovascular Diseases: A Trend?

Abstract Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and non-invasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive screening of heart diseases. Smartphones are widely used by the population and are perfect tools for screening cardiovascular diseases. They are equipped with camera, flashlight, microphone, processor, and internet connection, which allow optical, electrical, and acoustic analysis of cardiovascular phenomena. Thus, when using signal processing and artificial intelligence approaches, smartphones may have predictive power for cardiovascular diseases. Here we present different smartphone approaches to analyze signals obtained from various methods including photoplethysmography, phonocardiograph, and electrocardiography to estimate heart rate, blood pressure, oxygen saturation (SpO2), heart murmurs and electrical conduction. Our objective is to present innovations in non-invasive diagnostics using the smartphone and to reflect on these trending approaches. These could help to improve health access and the screening of cardiovascular diseases for millions of people, particularly those living in needy areas.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

[Acute kidney injury - Update 2021]. / Akute Nierenschädigung ­ Update 2021.

One in five hospitalized patients suffers acute kidney injury (AKI). Depending on its severity, AKI is associated with an up to 15-fold increased risk of mortality and constitutes a major risk factor for subsequent cardiovascular events and for the development of chronic kidney disease. This concise review summarizes recently published studies, focusing on 1.) automated AKI detection using electronic health records-based AKI alert systems, 2.) renal replacement therapy and its optimal timing and anticoagulation regimen, and 3.) coronavirus disease-2019 (COVID-19) associated AKI.

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

Artificial intelligence: a disruptive tool for a smarter medicine.

OBJECTIVE: Although highly successful, the medical R&D model is failing at improving people's health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI) could overcome these obstacles. Because AI will play a key role in this new collective intelligence, it is necessary that those involved in healthcare have a general knowledge of how these technologies work. With this comprehensive review, we intend to provide it. MATERIALS AND METHODS: A broad-ranging search has been undertaken on institutional and non-institutional websites in order to identify relevant papers, comments and reports. RESULTS: We firstly describe the flaws and defects of the current R&D biomedical model and how the generation of a new collective intelligence will result in a better and wiser medicine through a truly personalized and holistic approach. We, then, discuss the new forms of data collection and data processing and the different types of artificial learning and their specific algorithms. Finally, we review the current uses and applications of AI in the biomedical field and how these can be expanded, as well as the limitations and challenges of applying these new technologies in the medical field. CONCLUSIONS: This colossal common effort based on a new collective intelligence will exponentially improve the quality of medical research, resulting in a radical change for the better in the healthcare model. AI, without replacing us, is here to help us achieve the ambitious goal set by the WHO in the Alma Ata declaration of 1978: "Health for All".

Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype.

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Big data-driven precision medicine: Starting the custom-made era of iatrology.

Precision medicine is a new therapeutic concept and method emerging in recent years. The rapid development of precision medicine is driven by the development of omics related technology, biological information and big data science. Precision medicine is provided to implement precise and personalized treatment for diseases and specific patients. Precision medicine is commonly used in the diagnosis, treatment and prevention of various diseases. This review introduces the application of precision medicine in eight systematic diseases of the human body, and systematically presenting the current situation of precision medicine. At the same time, the shortcomings and limitations of precision medicine are pointed out. Finally, we prospect the development of precision medicine.

[Research status and trend of artificial intelligence in the diagnosis of urinary diseases].

Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.

Discovering hidden information in biosignals from patients using artificial intelligence.

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

The future of endoscopy: Advances in endoscopic image innovations.

The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

CAD and AI for breast cancer-recent development and challenges.

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.

[The future of computer-aided diagnostics in chest computed tomography]. / Perspektivy ispol'zovaniia kompleksnoi komp'iuter-assistirovannoi diagnostiki v otsenke struktur grudnoi kletki.

Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. The primary condition for machine learning is met because medical diagnostic images have high reproducibility. Today, the most common anatomic area for computed tomography is the thorax, particularly with the widespread lung cancer screening programs using low-dose computed tomography. In this regard, the amount of information that needs to be processed by a radiologist is snowballing. Thus, automatic image analysis will allow more studies to be interpreted. This review is aimed at highlighting the possibilities of machine learning in the chest computed tomography.