12 Appendix: Using the Opticare AI Camera

12.1 Introduction

The Opticare AI camera combines sophisticated technology with user-friendly operation to provide quick, non-invasive retinal imaging. This chapter provides comprehensive guidance for getting the most out of your device, from initial setup through routine operation and maintenance.

12.2 Device Overview

12.2.1 Physical Specifications

Dimensions: 297mm × 253mm × 125mm
Weight: 2 kg
Minimum pupil diameter requirement: 2.8 mm
Field angle: 40 degrees
Camera resolution: 12 million pixels

12.2.2 Key Components

Power indicator light
Micro HDMI interface
USB-B interface

12.3 Initial Setup

12.3.1 Equipment Requirements

Stable table or cart
Power outlet
Reliable internet connection
(Optional) Computer or tablet with Windows 10 or higher
USB cable (provided)
Power adapter (provided)

Follow these steps to get started:

Unpack the Camera: Open the case, take out the camera and the stand.
Set Up the Stand: Unfold the stand. Ensure the QR code is facing front. You can take out the QR code if you choose to not letting the users to scan themselves.

Set up the stand with the QR code facing front

Remove the key The camera is locked for transportation. Unlock by removing the screw key located at the bottom of the device.

Twist the key counter-clockwise to remove it

Save the key!

You will need the key when you pack the camera for transportation, so put it in a place where you won’t lose it.

Power Connection: Connect the power adaptor and switch on the power located on the left side of the camera. The green indication led should be on.

Connect the power connector to the side of the camera

Initialization: Wait for the camera to initialize and prompt you for the next steps.

Note

The camera is pre-configured with your Wi-Fi network. You should hear the message: “Connected to the network,” confirming it is connected to your Wi-Fi. Please refer to the email for the Wi-Fi that the device is configured with.

Unlock the Camera: Press the larger white button on the right side of the camera three times quickly. This unlocks the camera. There is a lock key under the camera that needs to be unscrewed for unlocking.

12.3.2 Environment Optimization

Room lighting: Moderate to dim
Temperature: Maintain between 5°C - 40°C
Humidity: Keep between 10% - 90%
Avoid direct sunlight on equipment
Ensure adequate ventilation

Cheung, Carol Yim-lui, Yi-Ting Ong, M. Kamran Ikram, Christopher Chen, and Tien Yin Wong. 2014. “Retinal Microvasculature in Alzheimer’s Disease.” Edited by Jack C. De La Torre. Journal of Alzheimer’s Disease 42 (s4): S339–52. https://doi.org/10.3233/JAD-141596.

Chua, Sharon Y. L., Anthony P. Khawaja, Andrew D. Dick, James Morgan, Baljean Dhillon, Andrew J. Lotery, Nicholas G. Strouthidis, et al. 2020. “Ambient Air Pollution Associations with Retinal Morphology in the UK Biobank.” Investigative Opthalmology & Visual Science 61 (5): 32. https://doi.org/10.1167/iovs.61.5.32.

Hua, Rong, Jianhao Xiong, Gail Li, Yidan Zhu, Zongyuan Ge, Yanjun Ma, Meng Fu, et al. 2022. “Development and Validation of a Deep Learning Algorithm Based on Fundus Photographs for Estimating the CAIDE Dementia Risk Score.” Age and Ageing 51 (12): afac282. https://doi.org/10.1093/ageing/afac282.

Kivipelto, Miia, Tiia Ngandu, Tiina Laatikainen, Bengt Winblad, Hilkka Soininen, and Jaakko Tuomilehto. 2006. “Risk Score for the Prediction of Dementia Risk in 20 Years Among Middle Aged People: A Longitudinal, Population-Based Study.” The Lancet Neurology 5 (9): 735–41. https://doi.org/10.1016/S1474-4422(06)70537-3.

Lin, Duoru, Jianhao Xiong, Congxin Liu, Lanqin Zhao, Zhongwen Li, Shanshan Yu, Xiaohang Wu, et al. 2021. “Application of Comprehensive Artificial Intelligence Retinal Expert (CARE) System: A National Real-World Evidence Study.” The Lancet Digital Health 3 (8): e486–95. https://doi.org/10.1016/S2589-7500(21)00086-8.

Ma, Yanjun, Jianhao Xiong, Yidan Zhu, Zongyuan Ge, Rong Hua, Meng Fu, Chenglong Li, et al. 2022. “Deep Learning Algorithm Using Fundus Photographs for 10-Year Risk Assessment of Ischemic Cardiovascular Diseases in China.” Science Bulletin 67 (1): 17–20. https://doi.org/10.1016/j.scib.2021.08.016.

Milea, Dan, Raymond P. Najjar, Zhubo Jiang, Daniel Ting, Caroline Vasseneix, Xinxing Xu, Masoud Aghsaei Fard, et al. 2020. “Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.” New England Journal of Medicine 382 (18): 1687–95. https://doi.org/10.1056/NEJMoa1917130.

Mitani, Akinori, Abigail Huang, Subhashini Venugopalan, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Yun Liu, and Avinash V. Varadarajan. 2019. “Detection of Anaemia from Retinal Fundus Images via Deeplearning.” Nature Biomedical Engineering 4 (1): 18–27. https://doi.org/10.1038/s41551-019-0487-z.

Nusinovici, Simon, Tyler Hyungtaek Rim, Hengtong Li, Marco Yu, Mihir Deshmukh, Ten Cheer Quek, Geunyoung Lee, et al. 2024. “Application of a Deep-Learning Marker for Morbidity and Mortality Prediction Derived from Retinal Photographs: A Cohort Development and Validation Study.” The Lancet Healthy Longevity, September, 100593. https://doi.org/10.1016/S2666-7568(24)00089-8.

Nusinovici, Simon, Tyler Hyungtaek Rim, Marco Yu, Geunyoung Lee, Yih-Chung Tham, Ning Cheung, Crystal Chun Yuen Chong, et al. 2022. “Retinal Photograph-Based Deep Learning Predicts Biological Age, and Stratifies Morbidity and Mortality Risk.” Age and Ageing 51 (4): afac065. https://doi.org/10.1093/ageing/afac065.

Tsukahara, Jason S., and Randall W. Engle. 2021. “Is Baseline Pupil Size Related to Cognitive Ability? Yes (Under Proper Lighting Conditions).” Cognition 211 (June): 104643. https://doi.org/10.1016/j.cognition.2021.104643.

Wong, Tien Yin. 2002. “Retinal Arteriolar Narrowing and Risk of Diabetes Mellitus in Middle-Aged Persons.” JAMA 287 (19): 2528. https://doi.org/10.1001/jama.287.19.2528.

Xia, Peng, Ming Hu, Feilong Tang, Wenxue Li, Wenhao Zheng, Lie Ju, Peibo Duan, Huaxiu Yao, and Zongyuan Ge. 2024. “Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations.” arXiv. http://arxiv.org/abs/2406.06384.

Zhao, Theodore, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Sid Kiblawi, Tristan Naumann, et al. 2024. “A Foundation Model for Joint Segmentation, Detection and Recognition of Biomedical Objects Across Nine Modalities.” Nature Methods, November. https://doi.org/10.1038/s41592-024-02499-w.

Zhu, Zhuoting, Danli Shi, Peng Guankai, Zachary Tan, Xianwen Shang, Wenyi Hu, Huan Liao, et al. 2023. “Retinal Age Gap as a Predictive Biomarker for Mortality Risk.” British Journal of Ophthalmology 107 (4): 547–54. https://doi.org/10.1136/bjophthalmol-2021-319807.