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Landon Diaz
Landon Diaz

Problem Solving In Neuroradiology PDF

He completed his radiology residency and fellowships in neuroradiology and musculoskeletal radiology in the Russell H. Morgan Department of Radiology and Radiological Science at the Johns Hopkins Hospital. He has served as an Adjunct Assistant Professor in the Department of Radiology at Johns Hopkins.

Problem Solving in Neuroradiology PDF


These three parts represent a life cycle of a machine learning model, which is presented in Fig. 4. The four basic machine learning categories are based on the problem and data: (1) supervised learning, or classification and regression of problems where data are labeled [36]; (2) unsupervised learning, or clustering and grouping of unlabeled data [37]; (3) semi-supervised learning, where unsupervised methods help supervised methods to increase accuracy [38, 39]; and (4) reinforcement learning, also known as learning through trials, where AI learns how to control an agent in the dynamic world [40]. Machine learning methods are often confused with statistical methods/models. Machine learning is all about results and conclusions, whereas statistical modeling and statistical methods are more about finding relationships and the significance of the relationships between variables.

Because of the success of machine learning, particularly deep learning [40], AI is experiencing an enormous renaissance, and successful radiology examples have been mentioned [67]; however, there are new challenges [68]. Considering AI systems as black boxes represents a major problem in terms of traceability and thus explainability.

Some of these problems are easy to solve, but some are nearly impossible. However, as more data sets are becoming available to the public, there will be more research in algorithms and approaches to obtain invariant data [79,80,81].

Studies are evidencing that humans are excellent at finding near-optimal solutions to difficult problems; they can detect and exploit some structural properties of the instance in order to enhance solution parts. It is interesting that medical doctors are not aware how hard and expensive it would be to solve these problems with AI [86, 87].

Knowledge about the disease area of interest and how aspects of this disease are linguistically expressed is useful and could promote better performing solutions. Whilst [139] find high variability between radiologists, with metric values (e.g. number of syntactic, clinical terms based on ontology mapping) being significantly greater on free-text than structured reports, [140] who look specifically at anatomical areas find less evidence for variability. Zech et al. [141] suggest that the highly specialised nature of each imaging modality creates different sub-languages and the ability to discover these labels (i.e. disease mentions) reflects the consistency with which labels are referred to. For example, edema is referred to very consistently whereas other labels are not, such as infarction/ischaemic. Understanding the language and the context of entity mentions could help promote novel ideas on how to solve problems more effectively. For example, [35] discuss how the accuracy of predicting malignancy is affected by cues being outside their window of consideration and [142] observe problems of co-reference resolution within a report due to long-range dependencies. Both these studies use traditional NLP approaches, but we observed novel neural architectures being proposed to improve performance in similar tasks specifically capturing long-range context and dependency learning, e.g., [31, 111]. This understanding requires close cooperation of healthcare professionals and data scientists, which is different to some other fields where more disconnection is present [125].

Most studies reviewed could be described as a proof-of-concept and not trialled in a clinical setting. Pons et al. [2] hypothesised that a lack of clinical application may stem from uncertainty around minimal performance requirements hampering implementations, evidence-based practice requiring justification and transparency of decisions, and the inability to be able to compare to human performance as the human agreement is often an unknown. These hypotheses are still valid, and we see little evidence that these problems are solved.

Most studies use retrospective data from single institutions but this can lead to a model over-fitting and, thus, not generalising well when applied in a new setting. Overcoming the problem of data availability is challenging due to privacy and ethics concerns, but essential to ensure that performance of models can be investigated across institutions, modalities, and methods. Availability of data would allow for agreed benchmarks to be developed within the field that algorithm improvements can be measured upon. External validation of applied methods was extremely low, although, this is likely due to the availability of external datasets. Making code available would enable researchers to report how external systems perform on their data. However, only 15 studies reported that their code is available. To be able to compare systems there is a need for common datasets to be available to benchmark and compare systems against.

Medical Physics faculty play an active role in equipment acquisition, such as site planning, shielding design and testing, and project management for new installations and equipment upgrades. We perform acceptance testing of all new equipment as well as periodic performance assessments. It is our goal to ensure continuous optimum operating characteristics, regulatory compliance and patient-specific quality assurance. The Medical Physics faculty engages in high-level problem-solving, covering all aspects of imaging equipment performance, at UMMC.

Business Analyst IVApril Carter began her career with Emory Crawford Long Hospital in 1997 as a mammography technologist. After years of working in mammography, she expanded her clinical and technical skills by working on the PACS team and eventually becoming an enterprise solutions architect for Information Technology. In her current role for Imaging Informatics as a Business Analyst IV, she enjoys troubleshooting, problem solving, workflow analysis, project coordination and process improvement working with the Department of Radiology and Imaging Services. April takes pride in supporting and developing solutions that positively impact patient care.

Dr. Trivedi is an Assistant Professor in the Departments of Radiology and Biomedical Informatics and a founding member of the Emory HITI lab ( His interests include the application of machine learning and data science towards solving problems in radiology, including breast cancer screening, natural language processing for radiology workflow, and prediction and patient outcomes. He also actively involved in progressing AI work within the Radiological Society of North America, Society for Skeletal Radiology, and the American College of Radiology.

Dr. Lall is the clinical lead for imaging informatics at Children's Healthcare of Atlanta and an assistant professor of radiology in the Pediatric Radiology Division and Neuroradiology Division. His research interests include pediatric neuroradiology, healthcare policy/economics, and practical applications of technology in healthcare. Dr. Lall is also actively involved in the American Society of Neuroradiology its Computer Science and Informatics committee, a member of the Data Sciences Institute Pediatric Panel of the American College of Radiology, and has served in multiple leadership positions in both organizations. 041b061a72


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