Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

【Objective】 To investigate the prevalence and influencing factors of overweight and obesity among preschool children in Suzhou. 【Methods】 A stratified cluster random sampling method was used to select 24 452 children aged 3 - 6 years in different districts of Suzhou from December 2021 to June 2022. Then the prevalence rate of overweight and obesity was determined by physical measurements. A case-control study was conducted with a questionnaire survey of 3 786 children(1 893 in the obesity group and 1 893 in the control group) to analyze the factors influencing preschool obesity. 【Results】 1) The overall detection rates of overweight among preschool children in Suzhou was 14.8%(boys 14.6%, girls 15.0%). The overall detection rates of obesity was 7.9%(boys 8.7%, girls 7.1%), with a statistically significant difference between boys and girls(χ²=19.828, P<0.01). 2) There was statistically significant difference in the detection rates of obesity among different age groups(χ²=98.415, P<0.01), with the lowest rate in the 3 - 4 years old group(5.8%) and the highest rate in the 6 - 7 years old group(11.8%). 3) The overall detection rates of mild, moderate and severe obesity was 4.8%, 2.6% and 0.5%, respectively. The proportion of moderate and severe obesity significantly increased with age(χ²=57.275, P<0.01). 4) Risk factors for preschool obesity included birth weight >4 000g, cesarean section, parental overweight/obesity, strong appetite of children, eating speed <10min/meal, high frequency of fried food consumption(>1time/week), eating while watching television, sedentary behavior >2h/d, insufficient exercise endurance, screen time >1h/d, and late bedtime(after 21∶30)(P<0.05). Protective factors for preschool obesity included larger breakfast consumption, fruits and vegetables as regular snacks, and physical activity after meals(P<0.05). 5) Factors influencing the degree of preschool obesity included paternal overweight(OR=1.33, 95%CI:1.06 - 1.65), paternal obesity(OR=1.91, 95%CI:1.46 - 2.49), maternal overweight(OR=1.25, 95%CI:1.01 - 1.54), maternal obesity(OR=1.94, 95%CI:1.40 - 2.69), low education level of father(junior high school or below)(OR=1.57, 95%CI:1.25 - 1.96), strong appetite of children(OR=1.72, 95%CI:1.41 - 2.11), eating speed <10min/meal(OR=1.29, 95%CI:1.05 - 1.57), sedentary behavior >2h/d(OR=1.51, 95%CI:1.24 - 1.85), insufficient exercise endurance(OR=1.56, 95%CI:1.12 - 2.19), and screen time>1h/d(OR=1.42, 95%CI:1.16 - 1.75). 【Conclusions】 The detection rates of overweight and obesity among preschool children in Suzhou are relatively high, and the detection rate and severity of obesity increase with age. In addition to genetic factors, preschool obesity are also associated with pregnancy and birth history, as well as unhealthy lifestyle after birth.