Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases

Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

Multispectral imaging in medicine: A bibliometric study

Multispectral Imaging has been used in many fields. In the medical field, Multispectral Imaging is still in its infancy. However, due to its excellent potential, it will also become one of the most important medical imaging in the future. This paper is the first bibliometric study in this field. The study comprehensively searched all relevant documents in Web of Science Core Collection from Jan 1, 1999 to Dec 31, 2022, systematically sorted out the author, journal, country and institution in this field, and analyzed the keywords. Based on this, the study suggests that researchers and healthcare workers should strengthen cooperation to apply Multispectral Imaging to more medical fields while further developing related technologies. At the same time, in the future, this field should focus on non-ex vivo tissue detection and the combination of Multispectral Imaging and artificial intelligence.

Label-Free Optical Technologies for Middle-Ear Diseases

Medical applications of optical technology have increased tremendously in recent decades. Label-free techniques have the unique advantage of investigating biological samples in vivo without introducing exogenous agents. This is especially beneficial for a rapid clinical translation as it reduces the need for toxicity studies and regulatory approval for exogenous labels. Emerging applications have utilized label-free optical technology for screening, diagnosis, and surgical guidance. Advancements in detection technology and rapid improvements in artificial intelligence have expedited the clinical implementation of some optical technologies. Among numerous biomedical application areas, middle-ear disease is a unique space where label-free technology has great potential. The middle ear has a unique anatomical location that can be accessed through a dark channel, the external auditory canal; it can be sampled through a tympanic membrane of approximately 100 microns in thickness. The tympanic membrane is the only membrane in the body that is surrounded by air on both sides, under normal conditions. Despite these favorable characteristics, current examination modalities for middle-ear space utilize century-old technology such as white-light otoscopy. This paper reviews existing label-free imaging technologies and their current progress in visualizing middle-ear diseases. We discuss potential opportunities, barriers, and practical considerations when transitioning label-free technology to clinical applications.

Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review

Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords 'otorhinolaryngology', 'imaging', 'computer vision', 'artificial intelligence', and 'deep learning', and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.

ISOM 2023 research Panel 4 - Diagnostics and microbiology of otitis media

OBJECTIVES

To identify and review key research advances from the literature published between 2019 and 2023 on the diagnosis and microbiology of otitis media (OM) including acute otitis media (AOM), recurrent AOM (rAOM), otitis media with effusion (OME), chronic suppurative otitis media (CSOM) and AOM complications (mastoiditis).

DATA SOURCES

PubMed database of the National Library of Medicine.

REVIEW METHODS

All relevant original articles published in Medline in English between July 2019 and February 2023 were identified. Studies that were reviews, case studies, relating to OM complications (other than mastoiditis), and studies focusing on guideline adherence, and consensus statements were excluded. Members of the panel drafted the report based on these search results.

MAIN FINDINGS

For the diagnosis section, 2294 unique records screened, 55 were eligible for inclusion. For the microbiology section 705 unique records were screened and 137 articles were eligible for inclusion. The main themes that arose in OM diagnosis were the need to incorporate multiple modalities including video-otoscopy, tympanometry, telemedicine and artificial intelligence for accurate diagnoses in all diagnostic settings. Further to this, was the use of new, cheap, readily available tools which may improve access in rural and lowmiddle income (LMIC) settings. For OM aetiology, PCR remains the most sensitive method for detecting middle ear pathogens with microbiome analysis still largely restricted to research use. The global pandemic response reduced rates of OM in children, but post-pandemic shifts should be monitored.

IMPLICATION FOR PRACTICE AND FUTURE RESEARCH

Cheap, easy to use multi-technique assessments combined with artificial intelligence and/or telemedicine should be integrated into future practice to improve diagnosis and treatment pathways in OM diagnosis. Longitudinal studies investigating the in-vivo process of OM development, timings and in-depth interactions between the triad of bacteria, viruses and the host immune response are still required. Standardized methods of collection and analysis for microbiome studies to enable inter-study comparisons are required. There is a need to target underlying biofilms if going to effectively prevent rAOM and OME and possibly enhance ventilation tube retention.

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

OBJECTIVE

To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.

DATA SOURCES

Web of Science, Embase, PubMed, and Cochrane Library.

REVIEW METHODS

Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.

RESULTS

Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.

CONCLUSION

This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.

Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence

A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.