ACR Appropriateness Criteria ® Tinnitus

Kessler, Marcus M.; Moussa, Marwan; Bykowski, Julie; Kirsch, Claudia; Aulino, Joseph M.; Berger, Kevin L.; Choudhri, Asim F.; Fife, Terry D.; Germano, Isabelle M.; Kendi, Ayse T.; Kim, Jeffrey H.; Luttrull, Michael D.; Nuñez, Diego; Shah, Lubdha M.; Sharma, Aseem; Shetty, Vilaas; Symko, Sophia C.; Cornelius, Rebecca S.

doi:10.1016/j.jacr.2017.08.052

Cited by 12 publications

(9 citation statements)

References 55 publications

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“…In addition, there is still controversy regarding the appropriate imaging of tinnitus[ 52 ]. Despite the consensus declaration of multiple medical societies[ 1 , 4 , 44 ], large-scale real-world evidence for quantifying the effectiveness of imaging results is urgently needed to justify their opinions. Therefore, it is necessary to promote the research into the application of NLP-based technology to tinnitus radiology reports.…”

Section: Discussionmentioning

confidence: 99%

“…The overall prevalence of tinnitus among the general population ranges from 10% to 14.5% [ 1 , 2 ], and 30% of people with tinnitus report ‘moderate’ to ‘very big’ difficulties in daily life [ 3 ]. There are a variety of conditions that can cause tinnitus, such as jugular bulb diverticulum, acoustic neuroma or defect of bone plate in sigmoid sinus.…”

Section: Introductionmentioning

confidence: 99%

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Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT)

Lin

Zhao

et al. 2022

BMC Med Inform Decis Mak

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Background Given the increasing number of people suffering from tinnitus, the accurate categorization of patients with actionable reports is attractive in assisting clinical decision making. However, this process requires experienced physicians and significant human labor. Natural language processing (NLP) has shown great potential in big data analytics of medical texts; yet, its application to domain-specific analysis of radiology reports is limited. Objective The aim of this study is to propose a novel approach in classifying actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer BERT-based models and evaluate the benefits of in domain pre-training (IDPT) along with a sequence adaptation strategy. Methods A total of 5864 temporal bone computed tomography(CT) reports are labeled by two experienced radiologists as follows: (1) normal findings without notable lesions; (2) notable lesions but uncorrelated to tinnitus; and (3) at least one lesion considered as potential cause of tinnitus. We then constructed a framework consisting of deep learning (DL) neural networks and self-supervised BERT models. A tinnitus domain-specific corpus is used to pre-train the BERT model to further improve its embedding weights. In addition, we conducted an experiment to evaluate multiple groups of max sequence length settings in BERT to reduce the excessive quantity of calculations. After a comprehensive comparison of all metrics, we determined the most promising approach through the performance comparison of F1-scores and AUC values. Results In the first experiment, the BERT finetune model achieved a more promising result (AUC-0.868, F1-0.760) compared with that of the Word2Vec-based models(AUC-0.767, F1-0.733) on validation data. In the second experiment, the BERT in-domain pre-training model (AUC-0.948, F1-0.841) performed significantly better than the BERT based model(AUC-0.868, F1-0.760). Additionally, in the variants of BERT fine-tuning models, Mengzi achieved the highest AUC of 0.878 (F1-0.764). Finally, we found that the BERT max-sequence-length of 128 tokens achieved an AUC of 0.866 (F1-0.736), which is almost equal to the BERT max-sequence-length of 512 tokens (AUC-0.868,F1-0.760). Conclusion In conclusion, we developed a reliable BERT-based framework for tinnitus diagnosis from Chinese radiology reports, along with a sequence adaptation strategy to reduce computational resources while maintaining accuracy. The findings could provide a reference for NLP development in Chinese radiology reports.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT)

Lin

Zhao

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Tympanometry suggested reduced mobility of the middle ear. Thus the initial optimal imaging strategy for this condition is to perform a CT temporal bone to assess the middle ear for the cause of pulsatile tinnitus (3). We decided to perform the scan in a post contrast phase to concurrently screen for any venous anomaly.…”

Section: Discussionmentioning

confidence: 99%

Superficial Middle Cerebral Vein: Tympanic Cavity Dehiscence Causing Pulsatile Tinnitus

Sing

Lim

See

et al. 2022

Otology &Amp; Neurotology

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“…Initial Imaging A pulsatile neck mass may reflect a normal tortuous artery, atypical lymphovascular malformation, arteriovenous fistula, pseudoaneurysm, paraganglioma, or other mass abutting an artery. Additional evaluation of vascular processes in the neck is addressed in the ACR Appropriateness Criteria Ò topic on "Cerebrovascular Disease" [16] and the ACR Appropriateness Criteria Ò topic on "Tinnitus" [17].…”

Section: Discussion Of Procedures By Variantmentioning

confidence: 99%

“…If the suspected origin of the neck mass is the thyroid gland, imaging should be guided by the ACR Appropriateness Criteria Ò topic on "Thyroid Disease" [15]. Additional evaluation of vascular processes in the neck is addressed in the ACR Appropriateness Criteria Ò topic on "Cerebrovascular Disease" [16] and the ACR Appropriateness Criteria Ò topic on "Tinnitus" [17]. Evaluation of neurological features associated with neck masses should be guided by the ACR Appropriateness Criteria Ò topic on "Plexopathy" [18].…”

Section: Summary Of Literature Review Introduction/backgroundmentioning

confidence: 99%