
Agriculture
November 22, 2024
AEST Agricultural Waste Charcoal Briquettes
Read SolutionImplemented by
Appropriate Energy Saving Technologies Limited (AEST)
Updated on June 6, 2024
·Created on October 12, 2020
A machine learning malarial retinopathy diagnostic tool, consisting of a 3D printed camera attachment and a mobile-powered application.
Cipher is a malarial retinopathy diagnostic tool with manufacturing headquarters in Lagos, Nigeria. The product is designed and manufactured by MicroFuse Technologies and aims to diagnose the disease with at least 90% accuracy.
Target SDGs
SDG 3: Good Health and Well-Being
Market Suggested Retail Price
$150.00
Target Users (Target Impact Group)
Public Sector Agencies
Distributors / Implementing Organizations
The manufacturer distributes the product.
Regions
Sub-Saharan Africa
Manufacturing/Building Method
The hardware component for this product is manufactured in Lagos, Nigeria. The accompanying application is deployed and available for most smartphone operating systems.
Intellectural Property Type
Patent
User Provision Model
Users can obtain the product directly from the manufacturer.
Distributions to Date Status
As of 2020, there are 20 implementations of this product with a further 700 potential customers.
Telecommunication service required
Yes
Level of connection service needed
None
Additional features required
Diagnostic tool supported with recommended next steps.
Device(s) required
Smart phone with camera capabilities
Permanent network connectivity required (Y/N)
Yes
Two way communication (Y/N)
Yes
Usage rate (%)
Unknown
Literacy support (Y/N)
Yes
Languages available
Unknown
Operating system and version
AI system built using DarkNet YOLO Machine Learning Algorithm for Deep Learning
Power requirements
Unknown
eHealth application
Telemedicine and health monitoring
Design Specifications
Cipher is a mobile application with data input through images captured through smartphone camera devices with a provided camera attachment. The images are processed through the artificial intelligence system built using the DarkNet YOLO Machine Learning Algorithm for Deep Learning, to determine the diagnosis of malarial retinopathy. The mobile application provides diagnosis and recommended next steps for the patient.
Technical Support
Technical support is provided in the user manual, with further support available by contacting the manufacturer.
Replacement Components
Replacement components for the camera attachment are available from the manufacturer.
Lifecycle
Unknown
Manufacturer Specified Performance Parameters
The manufacturer quotes the following performance targets: level of accuracy (<90%), efficiency, speed of diagnosis, ease of use, non-invasive.
Vetted Performance Status
Unknown
Safety
No known safety hazards related to this product.
Complementary Technical Systems
The mobile application provides diagnosis as well as the next steps following results.
Academic Research and References
None
Compliance with regulations
Standards ISO13485:1996, ISO13488:1996, ISO 14971
Evaluation methods
The product is evaluated for 90% accuracy for diagnosis as an evaluation. The manufacturer also cites non-invasive and speed, of diagnosis as evaluation criteria.
Other Information
Cipher was a product entered in the ASME iSHOW Kenya 2020.
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