Automated olfactory bulb segmentation on high resolutional T2-weighted MRI

Estrada, Santiago; Lu, Ran; Diers, Kersten; Zeng, Weiyi; Ehses, Philipp; Stöcker, Tony; Breteler, Monique M.B.; Reuter, Martin

doi:10.1016/j.neuroimage.2021.118464

Cited by 9 publications

(15 citation statements)

References 61 publications

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“…Of the 79 articles identified, 3 were published in 2017, 17–19 5 were published in 2018, 20–24 9 were published in 2019, 25–33 15 were published in 2020, 34–48 31 were published in 2021, 49–79 and 16 were published in 2022 80…”

Section: Resultsmentioning

confidence: 99%

“…Articles were published in journals of otolaryngology (n = 19), 17,18,23,26,28,30,36,38,39,42,46,52,57,65,72,77,86,87,94 radiology (n = 21), 19,22,25,29,31,33,34,45,48,51,54,55,58,67,73,74,81,83–85,95 medical sciences (n = 19), 32,41,44,47,53,61,63,66,68–70,76,78–80,89–91,93 or other areas of medicine (n = 20). 20,21,24,27,35,37,…”

Section: Resultsmentioning

confidence: 99%

“…Radiology Diagnostics There were 42 articles that used an application of AI for radiology/imaging diagnostics in rhinology. 19,21,22,25,26,29,31,38–43,45,46,48–51,54,55,58,59,62–64,67,69,75,78,80–86,89,91–93,95 Imaging tools used include CT (n = 25), 21,22,25,26,38–43,45,46,55,58,59,63,69,75,81–83,89,91,92,…”

Section: Resultsmentioning

confidence: 99%

“…There were 42 articles that used an application of AI for radiology/imaging diagnostics in rhinology. MRI (n = 10), 19,[48][49][50][51]78,80,84,86,93 and X-ray (n = 6), 29,31,54,62,64,85 with 1 article using both CT and MRI. 67 ML techniques including CNNs and deep learning were utilized by all 42 studies.…”

Section: Radiology Diagnosticsmentioning

confidence: 99%

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A Scoping Review of Artificial Intelligence Research in Rhinology

Kaul

Alvarado

Katsoulotos

et al. 2023

Am J Rhinol�Allergy

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Background A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving. Objective This scoping review aims to provide a brief overview of all current literature on AI in the field of rhinology. Further, it aims to highlight gaps in the literature for future rhinology researchers. Methods OVID MEDLINE (1946-2022) and EMBASE (1974-2022) were searched from January 1, 2017 until May 14, 2022 to identify all relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was used to guide the review. Results A total of 2420 results were identified of which 62 met the eligibility criteria. A further 17 articles were included through bibliography searching, for a total of 79 articles on AI in rhinology. Each year resulted in an increase in the number of publications, from 3 articles published in 2017 to 31 articles published in 2021. Articles were produced by authors from 22 countries with a relative majority coming from the USA (19%), China (19%), and South Korea (13%). Articles were placed into 1 of 5 categories: phenotyping/endotyping (n = 12), radiological diagnostics (n = 42), prognostication (n = 10), non-radiological diagnostics (n = 7), surgical assessment/planning (n = 8). Diagnostic or prognostic utility of the AI algorithms were rated as excellent (n = 29), very good (n = 25), good (n = 7), sufficient (n = 1), bad (n = 2), or was not reported/not applicable (n = 15). Conclusions AI is experiencing an increasingly significant role in rhinology research. Articles are showing high rates of diagnostic accuracy and are being published at an almost exponential rate around the world. Utilizing AI in radiological diagnosis was the most published topic of research, however, AI in rhinology is still in its infancy and there are several topics yet to be thoroughly explored.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Radiology Diagnosticsmentioning

confidence: 99%

See 2 more Smart Citations

A Scoping Review of Artificial Intelligence Research in Rhinology

Kaul

Alvarado

Katsoulotos

et al. 2023

Am J Rhinol�Allergy

View full text Add to dashboard Cite

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“…We performed OB segmentation on high-resolution T2-weighted images leveraging a novel fully-automated deep learning-based pipeline specifically designed for OB volumetry (28). All resulting segmentations underwent visual quality assessment.…”

Section: Olfactory Bulb Segmentationmentioning

confidence: 99%

Reduced Olfactory Bulb Volume Accompanies Olfactory Dysfunction After Mild SARS-CoV-2 Infection

Petersen

Becker

Schell

et al. 2022

Preprint

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Background: Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain opaque. Olfactory bulb volumetry has been previously established as a promising surrogate marker of smelling function in multiple otorhinolaryngological diseases. In this work, we aimed to elucidate the correspondence between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment. Therefore, we conducted a large-scale magnetic resonance imaging (MRI)-based investigation of individuals recovered from mainly mild to moderate COVID-19. Methods: Data of 233 COVID-19 convalescents from the Hamburg City Health Study COVID Program were analyzed. Upon recruitment, patients underwent cranial MR imaging and assessment of neuropsychological testing. Automated olfactory bulb volumetry was performed on T2-weighted MR imaging data. Olfactory function was assessed longitudinally after recruitment and at follow-up via a structured questionnaire. Follow-up assessment included quantitative olfactometric testing with Sniffin Sticks. Group comparisons of olfactory bulb volume and olfactometric scores were performed between individuals with and without smelling impairment. The associations of olfactory bulb volume and neuropsychological as well as olfactometric scores were assessed via multiple linear regression. Results: Longitudinal assessment demonstrated a declining prevalence of olfactory dysfunction from 67.6% at acute infection, 21.0% at baseline examination (on average 8.31 +- 2.77 months post infection) and 17.5% at follow-up (21.8 +- 3.61 months post infection). Participants with post-acute olfactory dysfunction had a significantly lower olfactory bulb volume [mm3] at scan-time than normally smelling individuals (mean +- SD, baseline: 40.76 +- 13.08 vs. 46.74 +- 13.66, f=4.07, p=0.046; follow-up: 40.45 +- 12.59 vs. 46.55 +- 13.76, f=4.50, p=0.036). Olfactory bulb volume successfully predicted olfactometric scores at follow-up (r_sp = 0.154, p = 0.025). Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Conclusions: Our work demonstrates the association of smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Olfactory bulb volume was demonstrably lower in individuals with sustained smelling impairment and predicted smelling function longitudinally. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.

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