Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Kamogashira, Teru; Fujimoto, Chisato; Kinoshita, Makoto; Kikkawa, Yuichiro; Yamasoba, Tatsuya; Iwasaki, Shinichi

doi:10.3389/fneur.2020.00007

Cited by 39 publications

(18 citation statements)

References 34 publications

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“…The gradient boosting classifier is a machine learning algorithm that has shown highly predictive performance in a wide range of practical applications and can identify the shortcomings of weak learners to optimize the model 37 . Studies have indicated that the gradient boosting classifier performs best among several machine learning algorithms in diagnosing epithelial ovarian cancer based on blood biomarkers 38 , characterizing the risk of type 2 diabetes mellitus 39 and predicting vestibular dysfunction 40 . In our modeling process, the gradient boosting classifier also achieved higher performance than other tested machine learning algorithms with an accuracy of 70.0% in STEM.…”

Section: Discussionmentioning

confidence: 99%

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

et al. 2021

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Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.

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Section: Discussionmentioning

confidence: 99%

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

et al. 2021

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“…Machine learning (ML) has previously been used and has shown an acceptable performance in predicting the characteristics and prognosis of ischemic stroke (8)(9)(10)(11). Several studies have shown that ML can be used to analyze nysagmogram or postulography videos to diagnose the causes of dizziness, which still needs equipment to measure the nystagmus or posture (12,13). Here, we used ML techniques to diagnose isolated acute dizziness patients visiting EMCs.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Acute Central Dizziness With Simple Clinical Information Using Machine Learning

et al. 2021

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Background: Acute dizziness is a common symptom among patients visiting emergency medical centers. Extensive neurological examinations aimed at delineating the cause of dizziness often require experience and specialized training. We tried to diagnose central dizziness by machine learning using only basic clinical information.Methods: Patients were enrolled who had visited an emergency medical center with acute dizziness and underwent diffusion-weighted imaging. The enrolled patients were dichotomized as either having central (with a corresponding central lesion) or non-central dizziness. We obtained patient demographics, risk factors, vital signs, and presentation (non-whirling type dizziness or vertigo). Various machine learning algorithms were used to predict central dizziness. The area under the receiver operating characteristic curve (AUROC) was measured to evaluate diagnostic accuracy. The SHapley Additive exPlanations (SHAP) value was used to explain the importance of each factor.Results: Of the 4,481 visits, 414 (9.2%) were determined as central dizziness. Central dizziness patients were more often older and male and had more risk factors and higher systolic blood pressure. They also presented more frequently with non-whirling type dizziness (79 vs. 54.4%) than non-central dizziness. Catboost model showed the highest AUROC (0.738) with a 94.4% sensitivity and 31.9% specificity in the test set (n = 1,317). The SHAP value was highest for previous stroke presence (mean; 0.74), followed by male (0.33), presentation as non-whirling type dizziness (0.30), and age (0.25).Conclusions: Machine learning is feasible for classifying central dizziness using demographics, risk factors, vital signs, and clinical dizziness presentation, which are obtainable at the triage.

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“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

Section: Discussionmentioning

confidence: 99%

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Tama

Kim

et al. 2020

Clin Exp Otorhinolaryngol

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Objectives. To present an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, with respect to opportunities, research challenges, and research directions.Methods. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles; we excluded non-English publications and duplicates, which resulted in a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments.Results. Most studies (42.22%, 38/90) used AI for image-based analysis, followed by clinical diagnosis and treatments (24 studies); each of the remaining two subcategories included 14 studies.Conclusion. Machine and deep learning have been extensively applied in the field of otorhinolaryngology. However, performance varies and research challenges remain.

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Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Abstract: To evaluate various machine learning algorithms in predicting peripheral vestibular dysfunction using the dataset of the center of pressure (COP) sway during foam posturography measured from patients with dizziness.

Cited by 39 publications

References 34 publications

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

Diagnosis of Acute Central Dizziness With Simple Clinical Information Using Machine Learning

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

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