Rigorously trained & tested. Clinically validated. Peer-reviewed.
Canvas Dx has been developed over years of testing and clinical validation with results published in peer-reviewed journals.
AI & Machine Learning that Supports Early Diagnosis
Canvas Dx is the first and only FDA authorized diagnostic system that gives more healthcare providers the ability to diagnose or rule out autism in children ages 1.5 to 6 years1. Learn about the key milestones of our product development, from foundational research to clinical validation, post-FDA authorization algorithmic optimization and real-world performance. Find summaries of all our major peer-reviewed scientific publications and journals.
- U.S. Food & Drug Administration. FDA Authorizes Marketing of Diagnostic Aid for Autism Spectrum Disorder https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-diagnostic-aid-autism-spectrum-disorder.
- Gordon-Lipkin, E., Foster, J. & Peacock, G. Whittling down the wait time: exploring models to minimize the delay from initial concern to diagnosis and treatment of autism spectrum disorder. Pediatr. Clin. 63, 851–859 (2016).
- Kosmicki, J. A., Sochat, V., Duda, M. & Wall, D. P. Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Transl. Psychiatry 5, e514–e514 (2015).
- Levy, S., Duda, M., Haber, N. & Wall, D. P. Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism. Mol. Autism 8, 1–17 (2017).
- Wall, D. P., Dally, R., Luyster, R., Jung, J.-Y. & DeLuca, T. F. Use of artificial intelligence to shorten the behavioral diagnosis of autism. (2012).PLoS One. 2012;7(8):e43855.
- Duda, M., Daniels, J. & Wall, D. P. Clinical evaluation of a novel and mobile autism risk assessment. J. Autism Dev. Disord. 46, 1953–1961 (2016).
- Duda, M., Kosmicki, J. A. & Wall, D. P. Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Transl. Psychiatry 4, e424–e424 (2014).
- Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E. & Fusaro, V. A. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl. Psychiatry 2, e100–e100 (2012).
- Tariq, Q. et al. Mobile detection of autism through machine learning on home video: A development and prospective validation study. PLoS Med. 15, e1002705 (2018).
- Kanne, S. M., Carpenter, L. A. & Warren, Z. Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening tool. Autism Res. 11, 1038–1049 (2018).
- Abbas, H., Garberson, F., Glover, E. & Wall, D. P. Machine learning approach for early detection of autism by combining questionnaire and home video screening. J. Am. Med. Inform. Assoc. 25, 1000–1007 (2018).
- Abbas, H., Garberson, F., Liu-Mayo, S., Glover, E. & Wall, D. P. Multi-modular AI approach to streamline autism diagnosis in young children. Sci. Rep. 10, 1–8 (2020).
- Megerian, J. T. et al. Evaluation of an Artificial Intelligence-Based Medical Device for Diagnosis of Autism Spectrum Disorder. Nat. Partn. J.- Digit. Med. (2022) doi:10.1038/s41746-022-00598-6.
- Brian, J. A., Zwaigenbaum, L. & Ip, A. Standards of diagnostic assessment for autism spectrum disorder. Paediatr. Child Health 24, 444–451 (2019).
- Wall, D. P., et al.Optimizing a de novo artificial intelligence-based medical device under a predetermined change control plan: Improved ability to detect or rule out pediatric autism,
Intelligence-Based Medicine, Volume 8, 2023, 100102, ISSN 2666-5212.