Machine Learning In Radiology Research Articles

Artificial Intelligence: A New Frontier in Radiological Imaging

Artificial intelligence (AI) is the development of computer systems that perform tasks that traditionally require human intelligence. One of the applications of AI is to help technologists and radiologists select appropriate patient protocols. Using AI methods, the accuracy of radiologists' diagnosis improved significantly by 37%. Currently, research is underway on the use of artificial intelligence in diagnostic medical imaging, which has demonstrated high sensitivity and accuracy in the identification of imaging abnormalities. In addition, artificial intelligence has the potential to improve tissue detection and characterization. Although the terms “artificial intelligence” and “machine learning” are often used interchangeably, it is important to note that machine learning is a specific subset of AI focusing on the use of algorithms to learn from the acquired data, enabling prediction, classification and understanding generation. With machine learning, a formal set of methodologies is based on solid mathematical foundations. The study of inventing and implementing algorithms that can learn from prior experiences is known as machine learning (ML). If you've observed a pattern of behaviour before, you can predict whether or not it'll happen again. That is, no prognosis can be made if no past examples exist. The major benefits of using machine learning in radiology will be the reduction of professional time and the accuracy of diagnostic outcomes. When compared to well-trained and experienced radiologists and technicians, several Al-based image segmentation methods in radiology systems have exhibited equivalent, if not better, performance.

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Introduction: Technological progress leads to an increasing use of radiological imaging, and an increase in the number of imaging results in an increased workload for radiologists. The driver of the application of AI in radiology is considered to be the reduction of the workload of radiologists and the need for faster and more accurate diagnosis. Aim: The aim of this paper is to bring the reader closer to the implementation of AI in radiology, especially in the MRI modality, and how deep learning algorithms improve image reconstruction. Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation has achieved, above all, high accuracy and precision, objectivity and time saving. Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.

Advancements in Radiology and Diagnostic Imaging

Radiology and diagnostic imaging have undergone remarkable advancements in recent years, shaping the future of healthcare and improving patient outcomes. This review article provides an extensive overview of the developments and opportunities in various aspects of radiology, including CT, MRI, ultrasound, digital radiology, teleradiology, 3D printing, radiomics, radiogenomics, and nuclear radiology. It highlights the integration of artificial intelligence and machine learning in radiology, the emergence of theranostics, and the exploration of the human microbiome. The article also delves into advanced imaging techniques for cardiovascular diseases, hybrid imaging modalities in oncology, and optical imaging. The summary emphasizes the importance of continued innovation and development in radiology and diagnostic imaging to enhance patient care and global health outcomes.

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review.

We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

Acceptance of artificial intelligence (AI) and machine learning (ML) among radiologists in Saudi Arabia

Artificial intelligence (AI) and machine learning (ML), in the new age of technological progress, provide huge benefits to every area of employment, ranging from IT to health care. To assess the knowledge of, attitude towards, and in-practice use of artificial intelligence and machine learning among radiology residents and faculty radiologists. A web-based questionnaire was distributed via Google Drive to 55 radiologists in the central region of the Kingdom of Saudi Arabia. The questionnaire comprised two sections: three questions regarding demographics and three questions regarding the knowledge, attitudes, and practices (KAP) of AI and ML in radiology. A total of 55 respondents (100%) completed the survey. The majority of respondents claimed familiarity with AI and ML (61.8%). Most radiologists (54.5%) expressed mixed feelings regarding the benefits of AI and ML applications in radiology. Regarding usability, a mixed response was received: 49.1% supported its usability and 45.5% were uncertain of the usability of AI and ML in radiology. Several studies have been conducted which have suggested the usability of AI and ML and their incorporation into the radiology department. The majority of radiologists in Saudi Arabia support the use of AI and ML. Further investigation into the usability of these tools is needed.

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.

Applications of Machine Learning in Radiology- A review

Applications of Machine Learning in Radiology- A review

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation.

Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques to develop new algorithms. Interestingly, this innovation is driven simultaneously by academia, existing global medical device vendors, and-fueled by venture capital-recently founded startups. Radiologists find themselves once again in the position to lead this innovation to improve clinical workflows and ultimately patient outcome. However, although the end of today's radiologists' profession has been proclaimed multiple times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this review article is to describe in detail the relevance of appropriate imaging data as a bottleneck for innovation, provide insights into the many obstacles for technical implementation, and give additional perspectives to radiologists who often view AI solely from their clinical role. As regulatory approval processes for such medical devices are currently under public discussion and the relevance of imaging data is transforming, radiologists need to establish themselves as the leading gatekeepers for evolution of their field and be aware of the many stakeholders and sometimes conflicting interests.

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.

Patient Perspectives and Priorities Regarding Artificial Intelligence in Radiology: Opportunities for Patient-Centered Radiology.

There is tremendous interest in the radiology community to apply artificial intelligence (AI) to improve radiology workflow and improve patient care [ 1 Jaremko J.L. Azar M. Bromwich R. et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019; 70: 107-118 Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar ]. The potential for AI in radiology is broad, ranging from patient scheduling, image acquisition and processing, triage of examinations for interpretation, lesion detection, lesion characterization, and automated reporting [ 2 Choy G. Khalilzadeh O. Michalski M. et al. Current applications and future impact of machine learning in radiology. Radiology. 2018; 288: 318-328 Crossref PubMed Scopus (273) Google Scholar ].

Will machine learning end the viability of radiology as a thriving medical specialty?

There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that ML algorithms will soon lead to the replacement of the radiologist. However, there are complex technological, regulatory, and medicolegal obstacles facing the implementation of machine learning in radiology that will definitely preclude replacement of the radiologist by these algorithms within the next two decades and beyond. While not a comprehensive review of machine learning, this article is intended to highlight specific features of machine learning which face significant technological and health care systems challenges. Rather than replacing radiologists, machine learning will provide quantitative tools that will increase the value of diagnostic imaging as a biomarker, increase image quality with decreased acquisition times, and improve workflow, communication, and patient safety. In the foreseeable future, we predict that today's generation of radiologists will be replaced not by ML algorithms, but by a new breed of data science-savvy radiologists who have embraced and harnessed the incredible potential that machine learning has to advance our ability to care for our patients. In this way, radiology will remain a viable medical specialty for years to come.

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets (“big data”), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract “radiomic” information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.

Machine Learning in Medical Imaging

Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.

Latest advances in artificial intelligence, computer-aided cancer detection and machine learning in radiology and oncology

Latest advances in artificial intelligence, computer-aided cancer detection and machine learning in radiology and oncology