New Approaches and Technologies to Improve Accuracy of Acute Otitis Media Diagnosis

Factors affecting the antimicrobial changes during treatment for acute otitis media in Japan: A retrospective cohort study using classification and regression trees (CART) analysis

OBJECTIVES

Factors that affect the change of first-line antimicrobial agents were investigated to further promote their appropriate use.

METHODS

This descriptive study used an electronic medical records database. Total 16,353 of the 199,896 patients enrolled between 1996 and 2019 met the inclusion criteria and formed the overall pediatric acute otitis media (AOM) cohort. The factors leading to the change in first-line antimicrobial agents within 14 days were analyzed using classification and regression trees (CART) analysis.

RESULTS

This antimicrobial treatment cohort, involved 4860 cases of AOM alone and 9567 cases of AOM with other diseases. The size of the medical facility based on number of beds and historical duration of patient registration impacted on antimicrobial changes.

CONCLUSIONS

The current results show that hospital-wide or nation-wide antimicrobial stewardship promotion could be the most influencing factor for antimicrobial changes. Particularly in cases of AOM where other diseases coexist, a more accurate diagnosis and definition of treatment failure of first-line drug are suggested to be important while establishing future treatment strategies. The current study is important to promote appropriate antimicrobial use for AOM treatment.

Smartphone-Based Artificial Intelligence for the Detection and Diagnosis of Pediatric Diseases: A Comprehensive Review

In recent years, the use of smartphones and other wireless technology in medical care has developed rapidly. However, in some cases, especially for pediatric medical problems, the reliability of information accessed by mobile health technology remains debatable. The main aim of this paper is to evaluate the relevance of smartphone applications in the detection and diagnosis of pediatric medical conditions for which the greatest number of applications have been developed. This is the case of smartphone applications developed for the diagnosis of acute otitis media, otitis media with effusion, hearing impairment, obesity, amblyopia, and vision screening. In some cases, the information given by these applications has significantly improved the diagnostic ability of physicians. However, distinguishing between applications that can be effective and those that may lead to mistakes can be very difficult. This highlights the importance of a careful application selection before including smartphone-based artificial intelligence in everyday clinical practice.

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.

Telehealth solutions for assessing auditory outcomes related to noise and ototoxic exposures in clinic and research

Nearly 1.5 billion people globally have some decline in hearing ability throughout their lifetime. Many causes for hearing loss are preventable, such as that from exposure to noise and chemicals. According to the World Health Organization, nearly 50% of individuals 12-25 years old are at risk of hearing loss due to recreational noise exposure. In the occupational setting, an estimated 16% of disabling hearing loss is related to occupational noise exposure, highest in developing countries. Ototoxicity is another cause of acquired hearing loss. Audiologic assessment is essential for monitoring hearing health and for the diagnosis and management of hearing loss and related disorders (e.g., tinnitus). However, 44% of the world's population is considered rural and, consequently, lacks access to quality hearing healthcare. Therefore, serving individuals living in rural and under-resourced areas requires creative solutions. Conducting hearing assessments via telehealth is one such solution. Telehealth can be used in a variety of contexts, including noise and ototoxic exposure monitoring, field testing in rural and low-resource settings, and evaluating auditory outcomes in large-scale clinical trials. This overview summarizes current telehealth applications and practices for the audiometric assessment, identification, and monitoring of hearing loss.

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images

Importance

Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients.

Objective

To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features.

Design, Setting, and Participants

A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features.

Main Outcomes and Measures

The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists.

Results

The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively.

Conclusions and Relevance

The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.