Evidence map›Paper›PMID 39230611›Full record

ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

Pakapoom Sukswai, Narit Hnoohom, Minh Phuoc Hoang, Songklot Aeumjaturapat, Supinda Chusakul, Jesada Kanjanaumporn, Kachorn Seresirikachorn, Kornkiat Snidvongs

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Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Pakapoom SukswaiDepartment of Otolaryngology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.ORCID http://orcid.org/0009-0000-2825-9039
Narit HnoohomDepartment of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
Minh Phuoc HoangDepartment of Otolaryngology, Hue University of Medicine and Pharmacy, Hue University, Hue, Vietnam.
Songklot AeumjaturapatEndoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Supinda ChusakulEndoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Jesada KanjanaumpornEndoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Kachorn SeresirikachornEndoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Kornkiat SnidvongsEndoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand. drkornkiat@yahoo.com.ORCID http://orcid.org/0000-0001-7537-4718

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

methodsData from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated.

resultsWhole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score.

conclusionUtilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

Indexed as

Deep LearningRhinitisSinusitisTomography, X-Ray ComputedAdultChronic DiseaseFemaleHumansMaleMaxillary SinusMiddle AgedMycosesRhinosinusitisROC CurveSensitivity and SpecificityComputed tomographyDeep learningFungal ballRhinosinusitisROC curve

Identifiers

PMID39230611

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.