Deep Learning Framework for Preoperative Recognition of Inverted Papilloma and Nasal Polyp

Ren, Tao; Li, Xinyao; Tian, Yang; Li, Wei

doi:10.1109/access.2021.3099687

Cited by 5 publications

(10 citation statements)

References 35 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%

“…The remaining 27 articles aimed to identify or differentiate between various pathologies 19,26,29,31,39,40,42,46,48,49,54,55,58,62–64,67,69,75,80–86,89 including concha bullosa (n = 1), 42 CRS (n = 3), 39,46,83 fungal ball (n = 1), 82 inverted papilloma (n = 3), 19,69,86 nasal cancers (n = 4), 19,49,63,86 maxillary defects (n = 1), 75 maxillary sinus lesions (n = 3), 55,58,81 maxillary sinusitis (n = 6), 29,31,54,62,64,85 nasal fractures (n = 1), 89 nasopharyngeal cancers (n = 4) 40,48,67,80 or other pathologies (n = 1), 84 and osteomeatal complex inflammation (n = 1). 26 Diagnostic accuracy was rated as excellent (n = 12), 39,40,42,46…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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: Resultsmentioning

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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“…In one study, Li et al ( 14 ) designed a deep learning framework through convolutional neural networks to automatically identify IP and NP with high AUC values of 0.95. In another study, Ren et al ( 15 ) used a deep convolutional neural network (CNN) which combines a densely connected convolutional network (DenseNet) and squeeze-and-excitation network (SENet) to classify IP and NP in CT and achieved a relatively high diagnostic value. Although these two study models gain excellent results but did not analyze IP and NP from a clinical perspective.…”

Section: Introductionmentioning

confidence: 99%

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

Yuan

Han

2023

Front. Neurol.

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Background and purposeInverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy.Materials and methodsA total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model.ResultsThere were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899.ConclusionA new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

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Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.Data SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.ConclusionThis state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI.

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Deep Learning Framework for Preoperative Recognition of Inverted Papilloma and Nasal Polyp

Cited by 5 publications

References 35 publications

A Scoping Review of Artificial Intelligence Research in Rhinology

A Scoping Review of Artificial Intelligence Research in Rhinology

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

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