A Robust Machine learning based method to classify normal and abnormal CT scan images of mastoid air cells

Khosravi, Mohammad Farough; Esmaeili, Mahdad; Moghaddam, Yalda Jabbari; Keshtkar, Ahmad; Jalili, Javad; Nasrabadi, Hamid Tayefi

doi:10.1007/s12553-022-00653-y

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…Like previous research [ 12 , 41 , 43 ], our analysis also certifies that stress raises the risk of DM (OR > 1). To clarify the causes of stress in Japan, the statistical data on reasons for stress (Appendix 5 ) in Japan were checked.…”

Section: Discussionsupporting

confidence: 89%

“…The top three reasons for stress in Japan were: disease and nursery, the problem of income balance, and problems with work. This tells us that the Japanese should be careful in easing the stress from ailments, income balance, and work, specifically for the stress from work, which was already certificated in previous research [ 12 , 41 , 43 ].…”

Section: Discussionmentioning

confidence: 87%

“…Fortunately, with the development of technology, various reliable and robust machine learning methods [43][44][45] were proposed and used to classify or predict complex risk factors of multiple diseases. Significantly, the Interpretable Machine Learning (IML) models will capture the "extraction of relevant knowledge from a machine-learning model concerning relationships either contained in data or learned by the model" [46].…”

Section: Introductionmentioning

confidence: 99%

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Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

2023

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Purpose Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen’s Survey of Living Conditions were analyzed using interpretable machine learning methods. Methods Seven interpretable machine learning methods were used to analysis Japan citizens’ census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi’s quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors. Results Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age. Conclusions New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

2023

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“…Recent studies have applied AI to otological imaging in various clinical contexts (Supplemental File 3, available online). These studies combined AI with otoscopy, [22][23][24][25][26]31,32,38,48,52,57,68,[74][75][76]79,81,84,85,93,95 computed tomography (CT), 30,36,41,42,50,62,63,70,71,88 and magnetic resonance imaging (MRI). 43,73,78 Most studies have focused on the image-based otoscopic diagnosis and automated segmentation of temporal bone CT for classifying normal and abnormal mastoid air cells.…”

Section: Application Of Ai In Otologymentioning

confidence: 99%

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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“…The processing of mastoid air cells is only partially represented on a CT scan by Olivier Cros [26]. In [27] a two-class (normal and abnormal) classifier based on convolutional neural networks deep learning model was introduced. The proposed model has an accuracy of 98.10%, however, this study classifies only normal and abnormal mastoids.…”

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confidence: 99%

Classification of mastoid air cells by CT scan images using deep learning method

et al. 2022

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Purpose Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model. Conclusions The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy.

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A Robust Machine learning based method to classify normal and abnormal CT scan images of mastoid air cells

Cited by 5 publications

References 19 publications

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

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

Classification of mastoid air cells by CT scan images using deep learning method

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