Emerging artificial intelligence applications in otological imaging

Abstract

Purpose of review To highlight the recent literature on artificial intelligence (AI) pertaining to otological imaging and to discuss future directions, obstacles and opportunities. Recent findings The main themes in the recent literature centre around automated otoscopic image diagnosis and automated image segmentation for application in virtual reality surgical simulation and planning. Other applications that have been studied include identification of tinnitus MRI biomarkers, facial palsy analysis, intraoperative augmented reality systems, vertigo diagnosis and endolymphatic hydrops ratio calculation in Meniere's disease. Studies are presently at a preclinical, proof-of-concept stage. Summary The recent literature on AI in otological imaging is promising and demonstrates the future potential of this technology in automating certain imaging tasks in a healthcare environment of ever-increasing demand and workload. Some studies have shown equivalence or superiority of the algorithm over physicians, albeit in narrowly defined realms. Future challenges in developing this technology include the compilation of large high quality annotated datasets, fostering strong collaborations between the health and technology sectors, testing the technology within real-world clinical pathways and bolstering trust among patients and physicians in this new method of delivering healthcare.

Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges

“…In recent years, AI technologies, such as machine learning (ML) and deep learning (DL), have found application in diverse facets of otolaryngology, spanning hearing loss, balance disorders, and investigations into skull base pathology [ 4 ]. Given the complexity of these conditions, coupled with the absence of a standardized diagnostic approach, there arises a need for a method that can provide precise interpretation of temporal bone (TB) imaging [ 5 ]. While the issuance of diagnostic and treatment guidelines has significantly contributed to this endeavor, their impact on daily clinical practice remains limited.…”

“…While the issuance of diagnostic and treatment guidelines has significantly contributed to this endeavor, their impact on daily clinical practice remains limited. Consequently, a fertile ground has emerged for the integration of AI into clinical settings [ 4 , 5 ]. This study aimed to discuss the latest advancements of AI in TB imaging, as well as to reflect on the challenges in the clinical implementation of ML in the investigation and management of lateral skull base pathology.…”

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

“…For instance, AI has demonstrated potential for broad applications in otolaryngology, ranging from the diagnosis of laryngeal cancer 3 to ear disease using otoscopic images. 4,5 While several reviews have examined AI and its applications in otolaryngology, 2,[6][7][8] rhinology, 9,10 otological images, 4,5,11 laryngeal cancer, 3,12 and head and neck cancer diagnosis, 13 some of them were based on the data from a few years ago and focused on only some diseases or specialties. With the emergence of new algorithms, it is important to update the literature and provide otolaryngologists with an overview of AI applications in otolaryngology.…”

“…This motivated the clinical application of AI to automate numerous medical tasks. For instance, AI has demonstrated potential for broad applications in otolaryngology, ranging from the diagnosis of laryngeal cancer 3 to ear disease using otoscopic images 4,5 …”

Augmented Reality Demonstration Survey Results From a Veteran Affairs Medical Center

“…Importantly, some researchers have raised concerns regarding the adaptability of the health care workforce with emerging technologies, and their interest in new methods of delivering care. 7,39 Successful deployment of any novel health care technology depends on multiple factors, including alignment with staff needs, receptivity to those solutions, customization to specific preferences, and usability. 1,3,[40][41][42] Unfortunately, the implementation of some health care technologies, such as electronic health records that did not account for end-user requirements, resulted in employee fatigue, burnout, and negative staffing turnover.…”