Abstract
The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing. This systematic review aims to highlight the current role of AI in temporal bone imaging. A Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE. The search algorithm employed consisted of key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.' Additionally, manual retrieval was conducted to capture any studies potentially missed in our initial search. All abstracts and full texts were screened based on our inclusion and exclusion criteria. A total of 72 studies were included. 95.8% were retrospective and 88.9% were based on internal databases. Approximately two-thirds involved an AI-to-human comparison. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item (37.5%). Fifty-eight out of 72 articles employed neural networks, with 72.2% using various types of convolutional neural network models. Quality assessment of the included publications yielded a mean score of 13.6 ± 2.5 on a 20-point scale based on the CONSORT-AI extension. Current research data highlight AI's potential in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. However, the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data. NA Laryngoscope, 2024.
Overview
- The study aims to highlight the current role of AI in temporal bone imaging. The methodology used for the experiment includes a systematic review of English publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE. The search algorithm employed key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.' The study includes 72 studies, with 95.8% being retrospective and 88.9% being based on internal databases. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item. The primary objective of the study is to highlight the potential of AI in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. The study aims to provide a comprehensive overview of the current state of AI in temporal bone imaging.
Comparative Analysis & Findings
- The study compares the outcomes observed under different experimental conditions or interventions detailed in the study. The results show that AI has the potential to enhance diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians. The study identifies that the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data.
Implications and Future Directions
- The study's findings highlight the significance of AI in temporal bone imaging and its potential impact on the field of research or clinical practice. The study identifies that the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data. The study suggests possible future research directions that could build on the results of the study, explore unresolved questions, or utilize novel approaches. The study also highlights the need for more standardized methodological approaches to ensure the consistency and reliability of future data.