Hearing recovery prediction and prognostic factors of idiopathic sudden sensorineural hearing loss: a retrospective analysis with a deep neural network model

Uhm, Tae Woong; Yi, Seongbaek; Choi, Sung-Won; Oh, Se-Joon; Kong, Soo-Keun; Lee, Il Woo; Lee, Hyun Min

doi:10.1016/j.bjorl.2023.04.001

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Other subfields such as otology, laryngology, and rhinology have also received artificial intelligence input in recent years. In patients with sudden sensorineural hearing loss, a study demonstrated an 88.81% accuracy rate of predicting audiometric recovery when trained with a neural language algorithm [30 ▪▪ ]. Similarly, a review of artificial intelligence applications in otology found that artificial intelligence was used most often to diagnose and manage vestibular disorders (29%) followed by prediction of sensorineural hearing loss outcomes (24%) in a dataset of 38 artificial intelligence articles [31].…”

Section: Applications In Maxillofacial and Facial Plastic And Reconst...mentioning

confidence: 99%

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

Fung,

Patel,

Tatum

2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. Recent findings Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. Summary Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.

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Section: Applications In Maxillofacial and Facial Plastic And Reconst...mentioning

confidence: 99%

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

Fung,

Patel,

Tatum

2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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“…Previous studies have developed machine learning models to accurately predict the prognosis of ISSHL [ 5 , 8 , 9 , 10 , 11 ]. The machine learning approach enables the analysis of extensive and intricate medical data, allowing for the extraction of concealed information that is often imperceptible to the human eye [ 9 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, various machine learning models were developed using clinical variables and targets indicating recovery from ISSHL. To determine this target, these studies consistently applied specific hearing frequency ranges—“0.5, 1, 2, and 3 kHz” or “0.5, 1, 2, and 4 kHz”—across all patients, assessing recovery according to Siegel’s criteria within these frequencies [ 9 , 10 , 11 ]. The level of hearing impairment is represented as the average hearing threshold across the defined frequency domains.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, for a more accurate evaluation of ISSHL recovery, it is imperative to consider the hearing-impaired frequency domains specific to each patient’s condition. Additionally, there was an excessive impact of hearing threshold on the ISSHL prognosis prediction of the machine learning model in previous studies [ 5 , 9 , 10 , 11 ]. The hearing thresholds of various hearing frequency domains, also known as pure tone audiometry (PTA) records, contribute to the prediction of machine learning models with high importance.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning

Shon,

Lim,

Chae

et al. 2024

Diagnostics

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Background: Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis one month after treatment, focusing on patient-specific hearing impairments. Methods: Patient-Personalized Seigel’s Criteria (PPSC) were developed considering patient-specific hearing impairment related to ISSHL criteria. We performed a statistical test to assess the shift in the recovery assessment when applying PPSC. The utilized dataset of 581 patients comprised demographic information, health records, laboratory testing, onset and treatment, and hearing levels. To reduce the model’s reliance on hearing level features, we used only the averages of hearing levels of the impaired frequencies. Then, model development, evaluation, and interpretation proceeded. Results: The chi-square test (p-value: 0.106) indicated that the shift in recovery assessment is not statistically significant. The soft-voting ensemble model was most effective, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.864 (95% CI: 0.801–0.927), with model interpretation based on the SHapley Additive exPlanations value. Conclusions: With PPSC, providing a hearing assessment comparable to traditional Seigel’s criteria, the developed models successfully predicted ISSHL recovery one month post-treatment by considering patient-specific impairments.

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Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning–based reconstruction

Kim,

Lee,

Lee

et al. 2024

Eur Radiol

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Hearing recovery prediction and prognostic factors of idiopathic sudden sensorineural hearing loss: a retrospective analysis with a deep neural network model

Cited by 5 publications

References 31 publications

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning

Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning–based reconstruction

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