Feng-Lei Zhu | Neuroscience | Best Researcher Award

Published on by China Scientist

Mr. Feng-Lei Zhu | Neuroscience | Best Researcher Award 

Feng-Lei Zhu, M.Med., is an attending physician and researcher at the Child Developmental and Behavioral Center of The Third Affiliated Hospital, Sun Yat-sen University. Specializing in autism spectrum disorder, Zhu integrates clinical expertise with advanced research in diagnostic innovation, cognitive assessment, and AI-assisted early screening.

Mr. Feng-Lei Zhu | Children Developmental and Behavioral Center, Third Affiliated Hospital of Sun Yat-sen University | China

Profile

SCOPUS

Education

  • Feng-Lei Zhu holds a Master of Medicine degree from Sun Yat-sen University in Guangzhou, China. This advanced medical education provided a strong foundation in pediatric developmental and behavioral health, enabling expertise in both clinical diagnosis and research related to child neurodevelopmental disorders.

Experience

  • Feng-Lei Zhu serves as an attending physician and researcher at the Child Developmental and Behavioral Center of The Third Affiliated Hospital, Sun Yat-sen University. In this role, the work integrates patient care with clinical research, focusing on early detection, accurate diagnosis, and comprehensive management of autism spectrum disorder and related developmental conditions in children.

Awards and Recognition

  • Feng-Lei Zhu has earned competitive funding support from major national and municipal science programs, including the National Natural Science Foundation of China and the Science and Technology Program of Guangzhou. These grants highlight the significance and innovation of the research contributions in the field of child developmental and behavioral medicine.

Skills and Expertise

  • The professional expertise includes advanced clinical diagnostic skills using DSM-5 criteria, ADI-R, and ADOS assessments, along with proficiency in WISC-IV cognitive evaluations. Analytical strengths cover Cox regression, machine learning applications, and multivariate statistical analysis, with extensive experience working with Chinese autism spectrum disorder cohorts ranging from toddlers to adolescents.

Research Focus 

  • Feng-Lei Zhu’s research centers on autism spectrum disorder in children, with a particular emphasis on diagnostic delay, multimodal machine learning tools for early screening, sex differences in symptom presentation, and the relationship between core symptoms and cognitive functioning. The work combines large-scale cohort studies, innovative AI-assisted evaluation methods, and in-depth statistical modeling to advance early diagnosis and improve clinical outcomes for children with autism in China.

Research Projects

  • Feng-Lei Zhu’s research centers on autism spectrum disorder in children, with a particular emphasis on diagnostic delay, multimodal machine learning tools for early screening, sex differences in symptom presentation, and the relationship between core symptoms and cognitive functioning. The work combines large-scale cohort studies, innovative AI-assisted evaluation methods, and in-depth statistical modeling to advance early diagnosis and improve clinical outcomes for children with autism in China.

Publications

Current situation and influencing factors of Chinese children’s diagnosis delay in autism
Authors: Zhu FL, Ji Y, Wang L, et al.
Journal: Journal of Neurodevelopmental Disorders

A multimodal machine learning system in early screening for toddlers with autism spectrum disorders
Authors: Zhu FL, Wang SH, Liu WB, et al.
Journal: Frontiers in Psychiatry

Examine sex differences in ASD in school-aged children with fluent language
Authors: Ji Y, Ji Y, Zhu FL*, et al. 
Journal: Frontiers in Psychiatry

Relationship between core symptoms and IQ in verbally fluent ASD children
Authors: Ji Y, Ji Y, Xu M, Zhu FL*.
Journal: Chinese Journal of Practical Pediatrics

 

Conclusion

  • Feng-Lei Zhu’s integrated approach to clinical care and research has significantly advanced understanding of autism spectrum disorder in Chinese children, bridging gaps between diagnosis, technology, and individualized intervention. By combining rigorous clinical expertise with innovative analytical methods, the work not only addresses critical challenges in early identification but also contributes to shaping evidence-based practices that can improve long-term outcomes for children and their families.