Abstract Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults). 

/pdf/detection-of-autism-spectrum-disorder-asd-in-children-and-3f7bcsuv.pdf

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

Deep Learning Applications in Surgery: Current Uses and Future Directions

Introduction: The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR’s application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction. Material and methods: A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024. Results: In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis. Conclusion: Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR’s immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR’s impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care. 

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies

OBJECTIVES: Autism spectrum disorder (ASD) is estimated to be ∼10 times higher in children with versus without an autistic sibling in population-based studies. Prospective studies of infant siblings have revealed even higher familial recurrence rates. In the current prospective longitudinal study, we provide updated estimates of familial ASD recurrence using a multinational database of infants with older autistic siblings. METHODS: Data were collated across 18 sites of the Baby Siblings Research Consortium, an international network studying the earliest manifestations of ASD. A total of 1605 infants with an older autistic sibling were followed from early in life to 3 years, when they were classified as ASD or non-ASD. Hierarchical generalized linear modeling, with site as a random effect, was used to examine predictors of recurrence in families and calculate likelihood ratios. RESULTS: A total of 20.2% of siblings developed ASD, which is not significantly higher than the previously reported rate of 18.7%. Male infant sex and &amp;gt;1 older affected sibling were significant predictors of familial recurrence. Proband sex also influenced recurrence rates, with siblings of female probands significantly more likely to develop ASD than siblings of male probands. Race and maternal education were also associated with recurrence in families. CONCLUSIONS: The familial recurrence rate of ASD, as measured in infant sibling studies, has not changed appreciably since previous estimates were made in 2011. Younger siblings of autistic children, particularly those who are male, have an affected female sibling, multiple affected siblings, or are impacted by social inequities, should be closely monitored and promptly referred for diagnostic evaluation. 

Familial Recurrence of Autism: Updates From the Baby Siblings Research Consortium

BACKGROUND
Intellectual ability predicts functional outcomes for children with autism spectrum disorder (ASD). It is essential to classify ASD children with and without intellectual disability (ID) to aid etiological research, provide services, and inform evidence-based educational and health planning.


METHODS
Using a cross-sectional study design, data from 2000 to 2016 active ASD surveillance among 8-year-olds residing in the New York-New Jersey Metropolitan Area were analyzed to determine ASD prevalence with and without ID. Multivariable Poisson regression models were used to identify trends for ASD with ID (ASD-I) and without ID (ASD-N).


RESULTS
Overall, 4661 8-year-olds were identified with ASD. Those that were ASI-I were 1505 (32.3%) and 2764 (59.3%) were ASD-N. Males were 3794 (81.4%), 946 (20.3%) were non-Hispanic Black (Black), 1230 (26.4%) were Hispanic, and 2114 (45.4%) were non-Hispanic white (white). We observed 2-fold and 5-fold increases in the prevalence of ASD-I and ASD-N, respectively, from 2000-2016. Black children were 30% less likely to be identified with ASD-N compared with white children. Children residing in affluent areas were 80% more likely to be identified with ASD-N compared with children in underserved areas. A greater proportion of children with ASD-I resided in vulnerable areas compared with children with ASD-N. Males had higher prevalence compared with females regardless of ID status; however, male-to-female ratios were slightly lower among ASD-I compared with ASD-N cases.


CONCLUSIONS
One-in-3 children with ASD had ID. Disparities in the identification of ASD without ID were observed among Black and Hispanic children as well as among children residing in underserved areas.

Prevalence and Disparities in the Detection of Autism Without Intellectual Disability.

The Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery

Methicillin-Resistant Staphylococcus Aureus Lip Abscess as a Complication of Facial Laceration

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Prevalence and Disparities in the Detection of Autism Without Intellectual Disability.

The Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery

Methicillin-Resistant Staphylococcus Aureus Lip Abscess as a Complication of Facial Laceration