Middle ear diseases such as otitis media and middle ear effusion, for which diagnoses are often delayed or misdiagnosed, are among the most common issues faced by clinicians providing primary care for children and adolescents. Artificial intelligence (AI) has the potential to assist clinicians in the detection and diagnosis of middle ear diseases through imaging. Otoendoscopic images obtained by otolaryngologists from Taipei Veterans General Hospital in Taiwan between Jany 1, 2011 to Dec 31, 2019 were collected retrospectively and de-identified. The images were entered into convolutional neural network (CNN) training models after data pre-processing, augmentation and splitting. To differentiate sophisticated middle ear diseases, nine CNN-based models were constructed to recognize middle ear diseases. The best-performing models were chosen and ensembled in a small CNN for mobile device use. The pretrained model was converted into the smartphone-based program, and the utility was evaluated in terms of detecting and classifying ten middle ear diseases based on otoendoscopic images. A class activation map (CAM) was also used to identify key features for CNN classification. The performance of each classifier was determined by its accuracy, precision, recall, and F1-score. A total of 2820 clinical eardrum images were collected for model training. The programme achieved a high detection accuracy for binary outcomes (pass/refer) of otoendoscopic images and ten different disease categories, with an accuracy reaching 98.0% after model optimisation. Furthermore, the application presented a smooth recognition process and a user-friendly interface and demonstrated excellent performance, with an accuracy of 97.6%. A fifty-question questionnaire related to middle ear diseases was designed for practitioners with different levels of clinical experience. The AI-empowered mobile algorithm's detection accuracy was generally superior to that of general physicians, resident doctors, and otolaryngology specialists (36.0%, 80.0% and 90.0%, respectively). Our results show that the proposed method provides sufficient treatment recommendations that are comparable to those of specialists. We developed a deep learning model that can detect and classify middle ear diseases. The use of smartphone-based point-of-care diagnostic devices with AI-empowered automated classification can provide real-world smart medical solutions for the diagnosis of middle ear diseases and telemedicine. This study was supported by grants from the Ministry of Science and Technology (MOST110-2622-8-075-001, MOST110-2320-B-075-004-MY3, MOST-110-2634-F-A49 -005, MOST110-2745-B-075A-001A and MOST110-2221-E-075-005), Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2, VGHUST111c-140), and Taipei Veterans General Hospital (V111E-002-3).

Sudden sensorineural hearing loss (SSNHL) is an emergency clinical otology condition defined as hearing loss greater than 30 dB over three consecutive frequencies within 72 h. We aimed to integrate pretreatment hearing grades with reports of treatment recovery outcomes of SSNHL using the modified Siegel's criteria. This was a retrospective cohort study comprising 110 patients with SSNHL seen between January 2014 and January 2015. The patients were treated with combined systemic and intra-tympanic steroid therapy. The audiometric results were recorded using the modified Siegel's criteria, including pretreatment hearing grades 1-5 (grade 1: hearing threshold under 25 dB, grade 2: hearing threshold 26-45 dB, grade 3: hearing threshold 46-70 dB, grade 4: hearing threshold 71-90 dB, grade 5: hearing threshold over 90 dB) and hearing recovery outcomes, consisting of complete recovery (CR), partial recovery (PR), slight improvement (SI), no improvement (NI) or non-serviceable ears (NS). Patients were further assessed based on the treatment modality (initial or salvage treatment) and duration of the treatment delay. Hearing improvements (CR + PR + SI) were seen in 56 patients. Patients with pretreatment hearing grade 3 had the highest improvement rate (88.2% or 30/34). Patients who received the combined steroid therapy as the initial modality had a better overall hearing improvement rate than did the salvage group. Treatment within the first 14 days yielded a better hearing improvement rate than did late treatments of more than 14 days, especially in patients with a pretreatment hearing grade of 5. Using the modified Siegel's criteria, we report the hearing recovery outcomes with matched pretreatment hearing grades of patients with SSNHL treated with combined intra-tympanic steroid therapy. Our results show the prognostic significance of pretreatment hearing grades in patients with SSNHL.

Background: Pure-tone screening (PTS) is considered as the gold standard for hearing screening programs in school-age children. Mobile devices, such as mobile phones, have the potential for audiometric testing. Objective: This study aimed to demonstrate a new approach to rapidly screen hearing status and provide stratified test values, using a smartphone-based hearing screening app, for each screened ear of school-age children. Method: This was a prospective cohort study design. The proposed smartphone-based screening method and a standard sound-treated booth with PTS were used to assess 85 school-age children (170 ears). Sound-treated PTS involved applying 4 test tones to each tested ear: 500 Hz at 25 dB and 1000 Hz, 2000 Hz, and 4000 Hz at 20 dB. The results were classified as pass (normal hearing in the ear) or fail (possible hearing impairment). The proposed smartphone-based screening employs 20 stratified hearing scales. Thresholds were compared with those of pure-tone average (PTA). Results: A total of 85 subjects (170 ears), including 38 males and 47 females, aged between 11 and 12 years with a mean (SD) of 11 (0.5) years, participated in the trial. Both screening methods produced comparable pass and fail results (pass in 168 ears and fail in 2 ears). The smartphone-based screening detected moderate or worse hearing loss (average PTA>25 dB) accurately. Both the sensitivity and specificity of the smartphone-based screening method were calculated at 100%. Conclusions: The results of the proposed smartphone-based self-hearing test demonstrated high concordance with conventional PTS in a sound-treated booth. Our results suggested the potential use of the proposed smartphone-based hearing screening in a school-age population.