Selected article for: "accuracy assess and machine learning model"

Author: Cha, Dongchul; Shin, Seung Ho; Kim, Jungghi; Eo, Tae Seong; Na, Gina; Bae, Seonghoon; Jung, Jinsei; Kim, Sung Huhn; Moon, In Seok; Choi, Jaeyoung; Park, Yu Rang
Title: Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study
  • Cord-id: e8utgxji
  • Document date: 2020_10_19
  • ID: e8utgxji
    Snippet: BACKGROUND: COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians. OBJECTIVE: This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types. METHODS: A total of 177 patients were prospectively enrolled, and the patient’s clinical manifestations with otoendoscopic images were written in the electr
    Document: BACKGROUND: COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians. OBJECTIVE: This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types. METHODS: A total of 177 patients were prospectively enrolled, and the patient’s clinical manifestations with otoendoscopic images were written in the electrical medical records. Asynchronous diagnoses were made for each patient to assess Top-1 and Top-2 accuracy, and we selected 20 cases to conduct a survey among four different otolaryngologists to assess the accuracy, interrater agreement, and diagnostic speed. We also constructed an experimental automated diagnosis system and assessed Top-1 accuracy and diagnostic speed. RESULTS: Asynchronous diagnosis showed Top-1 and Top-2 accuracies of 77.40% and 86.44%, respectively. In the selected 20 cases, the Top-2 accuracy of the four otolaryngologists was on average 91.25% (SD 7.50%), with an almost perfect agreement between them (Cohen kappa=0.91). The automated diagnostic model system showed 69.50% Top-1 accuracy. Otolaryngologists could diagnose an average of 1.55 (SD 0.48) patients per minute, while the machine learning model was capable of diagnosing on average 667.90 (SD 8.3) patients per minute. CONCLUSIONS: Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians.

    Search related documents:
    Co phrase search for related documents
    • accuracy assess and low quality: 1, 2, 3
    • accuracy assess and low sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • accuracy assess and magnetic resonance: 1, 2, 3, 4
    • accuracy calculate and low sensitivity: 1
    • accuracy high degree and low quality: 1
    • accuracy high degree and magnetic resonance: 1
    • accuracy low and low diagnostic accuracy: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • accuracy low and low quality: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • accuracy low and low sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
    • accuracy low and magnetic resonance: 1
    • accuracy low despite and low sensitivity: 1
    • accuracy regard and low quality: 1
    • accurate diagnosis and low quality: 1, 2, 3, 4, 5, 6
    • accurate diagnosis and low sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
    • accurate diagnosis and magnetic resonance: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20