Severe Acute Malnutrition (SAM) Photo Diagnosis App® Project: Phase 1
Phase 1. Pilot phase: Generating knowledge
This first phase of the project included two key streams of work:
- Generation of the algorithm and templates used for diagnosis.
- Development of the software application for both desktop and mobile phone applications.
The study of the Spanish sample allowed the design of morphometric landmark-based 2D models to describe, for the first time, the shape of the body of children aged 6-59 months. When diagnosing SAM by applying such models, an accuracy of SAM diagnosis of 100% was reached when analysing parts of the body separately.
A smartphone app prototype, containing the created models and algorithms required to obtain an in situ diagnosis was developed. This prototype permits the systematic registration of photographs as well as image processing, and application of a diagnostic algorithm. A methodology to code individuals was also included, to allow sending and receiving data through the app as well as data storage in a web service. An auxiliary desktop app was also developed to manage and configure the mobile app for the generation of templates and for image processing obtained from digital photographs for future research.
There is a further need to continue development of the app to study the accuracy of diagnosis for children aged 6-59 months in other populations beyond Senegal, and to improve the operability of the app by using more focused areas of the body. With the major hurdle of app and model development now done, it can now be further improved, and diagnostic accuracy refined as a larger sample is inputted into the software.
Laura Medialdea Principal Investigator of SAM Photo Diagnosis App® Project, Research Expert, Technical Department, ACF-Spain email@example.com