Review and update of temporal bone imaging

Gomes, Regina Lúcia Elia

doi:10.1590/0100-3984.2019.52.2e2

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Las reconstrucciones complementarias en diferentes planos (vistas de Pöschl, Stenvers y Stenvers modificada) pueden ayudar a visualizar los componentes estructurales del oído interno -como el acueducto vestibular y los canales semicirculares-, así como la evaluación de la posición del electrodo posquirúrgico posterior a la inserción de un implante coclear (9,10). Técnicas más nuevas, como la TAC de haz cónico, descritas en la literatura proporcionan una resolución espacial más alta con dosis de radiación más bajas (11).…”

Section: Valoración Imagenológica De La Pérdida Auditiva: La Relevanc...unclassified

Escuchar con los ojos: evaluación por tomografía axial computarizada de la pérdida auditiva

Jaramillo

Sandoval

Morera

et al. 2022

Rev. colomb. radiol.

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Múltiples patologías pueden afectar el funcionamiento del aparato auditivo. El conocimiento anatómico de los compartimentos del hueso temporal en la tomografía axial computarizada (TAC) conduce a una mejor comprensión de estas entidades y mejora el enfoque clínico hacia la pérdida auditiva conductiva o neurosensorial. Un diagnóstico preciso significa un inicio rápido del tratamiento, lo cual puede modificar el pronóstico del paciente.

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Section: Valoración Imagenológica De La Pérdida Auditiva: La Relevanc...unclassified

Escuchar con los ojos: evaluación por tomografía axial computarizada de la pérdida auditiva

Jaramillo

Sandoval

Morera

et al. 2022

Rev. colomb. radiol.

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“…However, as radiology reports offer a comprehensive description of visible lesions, lesions related to tinnitus account for only a small proportion compared with commonly reported degeneration or chronic lesions [ 4 ]. Especially in elderly patients, physicians may fail to notice such findings for many reasons, such as lack of experience in diagnosis, moreover, classifying findings correlated with tinnitus requires high expertise in ENT radiology [ 5 ].…”

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

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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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Cross-Sectional Imaging Evaluation of Congenital Temporal Bone Anomalies: What Each Radiologist Should Know

Nada

Agunbiade

Whitehead

et al. 2021

Current Problems in Diagnostic Radiology

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Review and update of temporal bone imaging

Cited by 3 publications

References 8 publications

Escuchar con los ojos: evaluación por tomografía axial computarizada de la pérdida auditiva

Escuchar con los ojos: evaluación por tomografía axial computarizada de la pérdida auditiva

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

Cross-Sectional Imaging Evaluation of Congenital Temporal Bone Anomalies: What Each Radiologist Should Know

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