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질문 1 의 8

Why are retinal images used as inputs in ophthalmic deep-learning research?

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  1. Why are retinal images used as inputs in ophthalmic deep-learning research?
    • They contain visible retinal structures that can be analyzed for specific research tasks
    • They provide a complete diagnosis of every systemic disease
    • They replace all clinical examinations
    • They guarantee equal performance across populations
  2. What regulatory action did the FDA take for IDx-DR in 2018?
    • Cleared it through a 510(k) as a generic camera
    • Granted a De Novo request and classified the device
    • Approved it as a drug
    • Granted an emergency-use authorization
  3. 많은 망막 AI 도구의 핵심 이미징 방법인 OCT란 무엇입니까?
    • 눈에 대한 혈액 검사
    • 콘택트렌즈의 일종
    • 망막 층의 미크론 해상도 단면을 제공하는 광학 스캔
    • 안압 측정 방법
  4. What did Poplin et al. report predicting from retinal fundus photographs?
    • A patient’s complete genome sequence
    • Exact medication adherence for the next year
    • Every future eye disease with certainty
    • Age, sex, smoking status, blood pressure and cardiovascular outcomes
  5. What did the RETFound paper evaluate?
    • A retinal-image foundation model pretrained on fundus photographs and OCT
    • A model trained only on chest radiographs
    • A surgical robot for retinal operations
    • A device that measures blood pressure from sound
  6. In the Moorfields-DeepMind paper, what were the two main network stages?
    • A speech recognizer followed by a language translator
    • A segmentation network followed by a classification network that gives diagnoses and referral recommendations
    • A tumor detector followed by a robot surgeon
    • A gene sequencer followed by a medication planner
  7. Which imaging modality did the Moorfields-DeepMind model analyze to recommend referrals for retinal disease?
    • Chest X-rays
    • Ultrasound images of the heart
    • Optical coherence tomography (OCT) scans
    • Skin photographs
  8. Which neural-network family did the Poplin retinal-photo study use?
    • A finite-state transducer
    • A nearest-neighbor graph search
    • A recurrent speech recognizer
    • A deep convolutional neural network