Image Capture Device for Remote Medical Examinations
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Solution Overview
Problem
Current consumer image capture devices lack the capability to ensure optimal image quality for remote medical examinations, particularly in capturing images of the eye, due to issues with object size and location within the frame, leading to suboptimal diagnostic assessments.
Innovation Solution
A system comprising an image capture device and a processor that uses neural networks to determine if an object (such as an iris or pupil) meets threshold values for size and location, and provides audio, visual, or haptic instructions to the user to adjust the image capture device or eye position for improved alignment and focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumer image capture devices are used for remote medical examinations, then image capture capability is available, but image quality for diagnostic purposes is insufficient due to improper object size and location in the frame
Solution Approach 1:
The system provides real-time feedback to the user through visual indicators showing the detected object's position and size relative to the frame, along with directional guidance indicating how to move the device or adjust positioning to achieve optimal alignment and focus for diagnostic quality images
Solution Approach 2:
The system performs preliminary detection and analysis of the target object (eye, iris, pupil) before final image capture, using neural networks to assess whether the object meets size and location criteria, and provides corrective guidance in advance to ensure diagnostic quality images are captured
2Measurement precision
If real-time image analysis is performed to ensure proper object size and location, then diagnostic accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial analysis by focusing neural network processing only on detecting and measuring the specific critical features (eye, iris, pupil) rather than analyzing the entire image in detail, enabling rapid assessment of whether the object meets capture criteria without exhaustive processing
Solution Approach 2:
The system performs preliminary detection using neural networks to quickly assess whether the target object meets size and location criteria before proceeding to full image capture and analysis, filtering out suboptimal images early in the process
Data Source
AI summary
A system may include an image capture device a processor communicatively coupled to the image capture device. The image capture device may be configured to obtain one or more images. The processor may be configured to receive a first image obtained by the image capture device, determine if a first object is in the first image, determine if a size and a location of the first object in the first image meet threshold values if the first object is determined to be in the first image, determine at least one corrective action to be taken if at least one of the size and the location of the first object in the image does not meet the threshold values, and cause an instruction for taking the at least one corrective action to be communicated to a user. Methods and machine-readable storage media also are disclosed.