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Bamiou DE, Kikidis D, Bibas T, Koohi N, Macdonald N, Maurer C, Wuyts FL, Ihtijarevic B, Celis L, Mucci V, Maes L, Van Rompaey V, Van de Heyning P, Nazareth I, Exarchos TP, Fotiadis D, Koutsouris D, Luxon LM. Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results. J Neurol 2022; 269:2584-2598. [PMID: 34669009 PMCID: PMC8527447 DOI: 10.1007/s00415-021-10829-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/17/2021] [Accepted: 09/28/2021] [Indexed: 11/01/2022]
Abstract
BACKGROUND Dizziness and imbalance are common symptoms that are often inadequately diagnosed or managed, due to a lack of dedicated specialists. Decision Support Systems (DSS) may support first-line physicians to diagnose and manage these patients based on personalised data. AIM To examine the diagnostic accuracy and application of the EMBalance DSS for diagnosis and management of common vestibular disorders in primary care. METHODS Patients with persistent dizziness were recruited from primary care in Germany, Greece, Belgium and the UK and randomised to primary care clinicians assessing the patients with (+ DSS) versus assessment without (- DSS) the EMBalance DSS. Subsequently, specialists in neuro-otology/audiovestibular medicine performed clinical evaluation of each patient in a blinded way to provide the "gold standard" against which the + DSS, - DSS and the DSS as a standalone tool (i.e. without the final decision made by the clinician) were validated. RESULTS One hundred ninety-four participants (age range 25-85, mean = 57.7, SD = 16.7 years) were assigned to the + DSS (N = 100) and to the - DSS group (N = 94). The diagnosis suggested by the + DSS primary care physician agreed with the expert diagnosis in 54%, compared to 41.5% of cases in the - DSS group (odds ratio 1.35). Similar positive trends were observed for management and further referral in the + DSS vs. the - DSS group. The standalone DSS had better diagnostic and management accuracy than the + DSS group. CONCLUSION There were trends for improved vestibular diagnosis and management when using the EMBalance DSS. The tool requires further development to improve its diagnostic accuracy, but holds promise for timely and effective diagnosis and management of dizzy patients in primary care. TRIAL REGISTRATION NUMBER NCT02704819 (clinicaltrials.gov).
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Affiliation(s)
- Doris-Eva Bamiou
- The Ear Institute, University College London, London, WC1X 8EE, UK.
- University College London Hospitals NHS Trust, London, UK.
- NIHR University College London Hospitals Biomedical Research Centre, London, UK.
| | - Dimitris Kikidis
- 1st Department of Otorhinolaryngology, Head and Neck Surgery, National and Kapodistrian University of Athens, Hippocrateion General Hospital, Athens, Greece
| | - Thanos Bibas
- 1st Department of Otorhinolaryngology, Head and Neck Surgery, National and Kapodistrian University of Athens, Hippocrateion General Hospital, Athens, Greece
| | - Nehzat Koohi
- The Ear Institute, University College London, London, WC1X 8EE, UK
- University College London Hospitals NHS Trust, London, UK
| | - Nora Macdonald
- University College London Hospitals NHS Trust, London, UK
| | - Christoph Maurer
- Clinic of Neurology and Neurophysiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Floris L Wuyts
- Antwerp University Research Centre for Equilibrium and Aerospace, University of Antwerp, Antwerp, Belgium
- Laboratory for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Berina Ihtijarevic
- Antwerp University Research Centre for Equilibrium and Aerospace, University of Antwerp, Antwerp, Belgium
- Department Otorhinolaryngology-Head and Neck Surgery, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Laura Celis
- Antwerp University Research Centre for Equilibrium and Aerospace, University of Antwerp, Antwerp, Belgium
- Department Otorhinolaryngology-Head and Neck Surgery, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Viviana Mucci
- Antwerp University Research Centre for Equilibrium and Aerospace, University of Antwerp, Antwerp, Belgium
- School of Science, Western Sydney University, Sydney, NSW, Australia
| | - Leen Maes
- Department of Rehabilitation Sciences, University of Ghent, Ghent, Belgium
| | - Vincent Van Rompaey
- Antwerp University Research Centre for Equilibrium and Aerospace, University of Antwerp, Antwerp, Belgium
- Department Otorhinolaryngology-Head and Neck Surgery, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Paul Van de Heyning
- Department Otorhinolaryngology-Head and Neck Surgery, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Irwin Nazareth
- Department of Primary Care and Population Health, University College London Medical School, London, UK
| | | | - Dimitrios Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Dimitrios Koutsouris
- Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece
| | - Linda M Luxon
- The Ear Institute, University College London, London, WC1X 8EE, UK
- University College London Hospitals NHS Trust, London, UK
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Artificial Intelligence Applications in Otology: A State of the Art Review. Otolaryngol Head Neck Surg 2020; 163:1123-1133. [DOI: 10.1177/0194599820931804] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Objective Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions. Data Sources Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology. Review Methods An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies. Conclusion The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years. Implications for Practice This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.
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