Updated on June 6, 2024

·

Created on October 12, 2020

Cipher

A machine learning malarial retinopathy diagnostic tool, consisting of a 3D printed camera attachment and a mobile-powered application.

Developed By Unknown
Content Partners
Unknown

Author

Product Description

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.

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.

Product Schematics

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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