ABOUT US
We are the AJA Group, consisting of Aline, Junior, and Amanda, students from Fatec Registro. Our goal is to contribute to SDG 3 – 'Good Health and Well-being.' With the rising rates of autism spectrum disorders, we decided to focus our studies on developing an innovative solution for this challenge during the Multiplatform Software Development course.
HOW IT WORKS
Identification of ASD Signs
It all starts with parents or guardians identifying the signs of ASD in the child. Then, they seek out a healthcare specialist.
The Importance of Professional Evaluation
A qualified healthcare professional will use SOFIA to assist in the pre-diagnosis, ensuring greater effectiveness and accessibility.
Innovation with Artificial Intelligence
Our app uses AI to analyze the completion of screening protocols and the child's demographic data to provide the result.
OUR DATA
More than 60% of children are diagnosed after the age of 4.
37% of these children see more than 5 doctors before getting a final diagnosis.
The main signs of ASD are stereotypies and communication disorders.
More than 60% of children are diagnosed after the age of 4.
37% of these children see more than 5 doctors before getting a final diagnosis.
The main signs of ASD are stereotypies and communication disorders.
THE VALE DO RIBEIRA
The Vale do Ribeira, located in the interior of SP/Brazil and composed of 22 municipalities, is a predominantly rural region. Applying the equivalence that 2.8% of the population is autistic (CDC), it is possible to calculate that there are more than 300 children between the ages of 0 and 9 with the disorder in the region. Our goal is to make the pre-diagnosis of ASD more accessible for children in rural areas and with low socioeconomic status, such as in the Vale do Ribeira.
WANT TO HELP SOFIA IN THIS MISSION?
Although we are an academic project aimed at studying and developing a solution, we are very proud of our results! Our next steps consist of improving the SOFIA AI with a computer vision model to analyze stereotypies and adding new screening protocols to cover other age groups, continuing our focus on the pre-diagnosis of childhood ASD.
BIBLIOGRAPHY
- GUTHRIE, W. et al. Accuracy of autism screening in a large pediatric network.
Pediatrics, American Academy of Pediatrics, v. 144, n. 4, 2019. - ROBINS, D. L. et al. A machine learning strategy for autism screening in toddlers.
Journal of developmental and behavioral pediatrics: JDBP, NIH Public Access, v. 40,n. 5, p. 369, 2019. - MEGERIAN, J.T. et al. Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. npj Digit. Med. 5, 57 (2022). https://doi.org/10.1038/s41746-022-00598-6. Acesso em: 09 de set. de 2023.
- DSM-5. MANUAL DIAGNÓSTICO E ESTATÍSTICO DE TRANSTORNOS MENTAIS, 5a EDIÇÃO. 2014. Disponível em: https://www.institutopebioetica.com.br/documentos/manual-diagnostico-e-estatistico-de-transtornos-mentais-dsm-5.pdf. Acesso em: 12 de set. de 2023.
- CDC. Screening and Diagnosis of Autism Spectrum Disorder for Healthcare Providers. 2022. Disponível em: https://www.cdc.gov/ncbddd/autism/hcp-screening.html. Acesso em: 26 de set. de 2023.
- ONU. Agenda 2030 para o Desenvolvimento Sustentável. 2015. Disponível em: https://brasil.un.org/pt-br/91863-agenda-2030-para-o-desenvolvimento-sustent%C3%A1vel. Acesso em: 18 de set. de 2023.