Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

Filice, Ross W.; Kahn, Charles Η.

doi:10.1007/s10278-019-00186-3

Cited by 14 publications

(15 citation statements)

References 17 publications

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“…As described above, the Foundational Model of Anatomy (FMA) forms much of the basis of the anatomical terms in RadLex. Knowledge of radiological differential diagnosis in RGO has been integrated with SNOMED CT, RadLex, DO, HPO, and the ORDO [16][17][18]. This integration allows one to pose new, abstract questions that relate diseases and their imaging phenotypes such as, "Which gastrointestinal system diseases may cause an abnormality of the genitourinary system?"…”

Section: Integration Of Ontologiesmentioning

confidence: 99%

Biomedical Ontologies to Guide AI Development in Radiology

Filice

Kahn

2021

J Digit Imaging

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The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain's terms through their relationships with other terms in the ontology. Those relationships, then, define the terms' semantics, or "meaning." Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machinecomputable. Some ontologies, such as RSNA's RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image-based machine learning, radiomics, and planning.

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Section: Integration Of Ontologiesmentioning

confidence: 99%

Biomedical Ontologies to Guide AI Development in Radiology

Filice

Kahn

2021

J Digit Imaging

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“…We have also found that certain key terms are missing from RadLex and manually extended it. Other approaches to mitigate this problem and to increase interoperability, aim to combine multiple (both radiology-specific and general medical) ontologies or procedural databases such as RadLex, LOINC/RSNA playbook, CDE from the RSNA and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as well as the International Classification of Diseases (v.10) Clinical Modification (ICD-10-CM) 56,[58][59][60] .…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

Maros

Cho

Junge

et al. 2021

Sci Rep

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Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.

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“…Additionally, many ontologies were developed to diagnose and treat diseases such as diabetes mellitus [40][41][42], liver cancer [43], myocardial infarction [44], dermatosis [45], hypertension [46], Ebola virus infection [47], fibrotic interstitial lung disease [48], breast cancer [49,50], pneumonia [51][52][53], and Alzheimer's [54]. Some ontologies are for differential diagnosis [55][56][57] and others for drugs [58][59][60].…”

Section: Ontologies and Semantic Reasoningmentioning

confidence: 99%

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

et al. 2020

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Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

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Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

Cited by 14 publications

References 17 publications

Biomedical Ontologies to Guide AI Development in Radiology

Biomedical Ontologies to Guide AI Development in Radiology

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

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