Source: Geralt / Aliexpress
New peer-reviewed studies published in npj digital medicineNature Portfolio Journal shows the results of clinical trials. artificial intelligence (AI) Machine learning software (SaMD) as a medical device helped primary care providers assess whether infants have autism spectrum disorders (ASD).
Autism is a systemic disorder with common comorbidities, including: anxiety, depression, Attention deficit And hyperactivity disorder (ADHD), schizophrenia, Bipolar disorder, Sleep disorders, gastrointestinal disorders, dietary and feeding problems, and seizures. Autism affects all ethnic groups, and boys are four times more likely to be diagnosed with autism than girls.
The World Health Organization (WHO) estimates that 1 in 100 children worldwide suffers from autism spectrum disorders. According to the Centers for Disease Control and Prevention (CDC) Autism and Developmental Disability Monitoring (ADDM) network, about 1 in 44 children aged 8 years in the United States was identified as having autism in 2018.
In this peer-reviewed study, the software was evaluated as a medical device called the Cognos ASD Diagnosis Aid. It leverages AI machine learning and consists of a mobile app for caregivers and a portal for video analytics and healthcare providers. Dr. Dennis Wall, Cognoa’s scientific founder and associate professor of Stanford Pediatric and Biomedical Data Science, created an AI machine learning algorithm from an interviewer-based examination performed by a clinician on a care provider, where he centered. I categorized the data about the symptoms of autism when I was in Japan. Bioinformatics at Harvard Medical School.
The machine learning algorithm was originally developed in Dr. Wall’s lab. AI was trained in the Autism Genetic Resource Exchange (AGRE) database at Autism Speaks and validated in the same database with data from the Simons Foundation and the Boston Autism Consortium. Dr. Wall’s AI classifier was 92% accurate in predicting people without ASD, according to a previous study published in 2012. AI has been further enhanced to include tools used by clinicians based on direct observation of the child, called autism. Diagnostic Observation Schedule (ADOS).
The version of AI evaluated in this current study was the 4th generation and was enhanced by subsequent research and development. This study evaluated the ability of AI-enabled devices to assist healthcare professionals in diagnosing autism spectrum disorders in children aged 18-72 months with parents or healthcare providers concerned about developmental delay. ..The predictions generated by machine learning devices were compared to human clinical diagnosis based on DSM-5 Criteria and one or more reviews Validated by a specialist clinician.
In 425 study participants, the AI machine learning algorithm made a diagnosis of either “ASD positive” or “ASD negative” in 32% of patients. AI prediction accuracy was 98.4 percent for children with autism and 78.9 percent for children without ASD. For 68% of children with AI-uncertain results, 91% had one or more neurodevelopmental states.
It is useful to note that one or more mental health conditions often accompany people with autism in order to contextualize the “uncertain” classification.according to Autism and Health: Special Report by Autism Speaks, Epidemiological studies estimate that 54-70 percent of people with autism have at least one mental health condition.According to Autism Speaks, the most common mental health conditions are autism teeth Note Deficit and hyperactivity disorder (ADHD). In various other studies conducted over the last decade, the report found that an estimated 30-61% of people with autism also suffer from ADHD, and about 6-7 of the general population with CDC ADHD numbers. It points out that it is much higher than%. An estimated 11-42% of people with autism have one or more anxiety disorders, 7% of children and 26% of adults have depression, 4-35% of adults have schizophrenia, and 6 and 27% have bipolar. It’s an obstacle.
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