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McInnis MG, Andreassen OA, Andreazza AC, Alon U, Berk M, Brister T, Burdick KE, Cui D, Frye M, Leboyer M, Mitchell PB, Merikangas K, Nierenberg AA, Nurnberger JI, Pham D, Vieta E, Yatham LN, Young AH. Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder. Bipolar Disord 2022; 24:499-508. [PMID: 35244317 PMCID: PMC9440950 DOI: 10.1111/bdi.13198] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Bipolar disorder (BD) is a complex and dynamic condition with a typical onset in late adolescence or early adulthood followed by an episodic course with intervening periods of subthreshold symptoms or euthymia. It is complicated by the accumulation of comorbid medical and psychiatric disorders. The etiology of BD remains unknown and no reliable biological markers have yet been identified. This is likely due to lack of comprehensive ontological framework and, most importantly, the fact that most studies have been based on small nonrepresentative clinical samples with cross-sectional designs. We propose to establish large, global longitudinal cohorts of BD studied consistently in a multidimensional and multidisciplinary manner to determine etiology and help improve treatment. Herein we propose collection of a broad range of data that reflect the heterogenic phenotypic manifestations of BD that include dimensional and categorical measures of mood, neurocognitive, personality, behavior, sleep and circadian, life-story, and outcomes domains. In combination with genetic and biological information such an approach promotes the integrating and harmonizing of data within and across current ontology systems while supporting a paradigm shift that will facilitate discovery and become the basis for novel hypotheses.
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Affiliation(s)
| | - Ole A. Andreassen
- NORMENT CentreUniversity of Oslo and Oslo University HospitalOsloNorway
| | - Ana C. Andreazza
- Department of Pharmacology & ToxicologyTemerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | | | - Michael Berk
- Deakin UniversityIMPACT – the Institute for Mental and Physical Health and Clinical TranslationSchool of MedicineBarwon HealthGeelongAustralia
- OrygenThe National Centre of Excellence in Youth Mental HealthCentre for Youth Mental HealthFlorey Institute for Neuroscience and Mental Health and the Department of PsychiatryThe University of MelbourneMelbourneAustralia
| | - Teri Brister
- National Alliance on Mental IllnessArlingtonVirginiaUSA
| | | | - Donghong Cui
- Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghai Mental Health CenterShangaiChina
| | | | - Marion Leboyer
- Département de psychiatrieUniversité Paris Est Creteil (UPEC)AP‐HPHôpitaux Universitaires H. MondorDMU IMPACTINSERM, translational NeuropsychiatryFondation FondaMentalCreteilFrance
| | | | - Kathleen Merikangas
- Intramural Research ProgramNational Institute of Mental HealthBethesdaMarylandUSA
| | | | | | - Daniel Pham
- Milken InstituteCenter for Strategic PhilanthopyWashingtonDistrict of ColumbiaUSA
| | - Eduard Vieta
- Bipolar and Depressive disorders UnitHospital ClinicInstitute of NeuroscienceUniversity of BarcelonaIDIBAPSCIBERSAMBarcelonaCataloniaSpain
| | | | - Allan H. Young
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and NeuroscienceKing’s College London & South London and Maudsley NHS Foundation TrustBethlem Royal HospitalBeckenhamKentUK
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Garau N, Orro A, Summers P, De Maria L, Bertolotti R, Bassis D, Minotti M, De Fiori E, Baroni G, Paganelli C, Rampinelli C. Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study. J Digit Imaging 2022; 35:970-982. [PMID: 35296941 PMCID: PMC9485502 DOI: 10.1007/s10278-022-00615-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022] Open
Abstract
Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.
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Affiliation(s)
- Noemi Garau
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy. .,Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| | - Alessandro Orro
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, Italy
| | - Paul Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Lorenza De Maria
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Raffaella Bertolotti
- Division of Data Management, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Danny Bassis
- School of Medicine, University of Milan, Milan, Italy
| | - Marta Minotti
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Elvio De Fiori
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Guido Baroni
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy.,Bioengineering Unit, CNAO Foundation, Pavia, Italy
| | - Chiara Paganelli
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy
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Filice RW, Kahn CE. Biomedical Ontologies to Guide AI Development in Radiology. J Digit Imaging 2021; 34:1331-1341. [PMID: 34724143 PMCID: PMC8669056 DOI: 10.1007/s10278-021-00527-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/27/2021] [Accepted: 10/13/2021] [Indexed: 10/25/2022] Open
Abstract
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 machine-computable. 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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Affiliation(s)
- Ross W Filice
- Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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Messaoudi R, Mtibaa A, Vacavant A, Gargouri F, Jaziri F. Ontologies for Liver Diseases Representation: A Systematic Literature Review. J Digit Imaging 2019; 33:563-573. [PMID: 31848894 DOI: 10.1007/s10278-019-00303-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia.
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France.
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Faïez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
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