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.

This study aims to detect early changes of kidney in patients with primary hypertension by 3.0 T functional magnetic resonance imaging (fMRI). 26 patients with primary hypertension (hypertension group) and 33 healthy volunteers (control group) underwent conventional and functional magnetic resonance scans, which included blood oxygen level-dependent (BOLD) MRI, diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). We measured renal cortical thickness (CT), parenchymal thickness (PT), and functional values of renal cortex and medulla including R2* value, apparent diffusion coefficient (ADC) value and fractional anisotropy (FA) value in each group, and then calculated the cortical/parenchymal thickness ratio (CPR). Compared with those in the control group, CT and CPR in hypertension group were larger (P < 0.01), cortical and medullar R2* values increased (P < 0.01) whereas medullar FA values decreased (P < 0.05). It could be well concluded that noninvasive 3.0 T functional MRI would have important clinical significance in identifying early abnormalities of kidney in hypertension patients.
Anisotropy ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Essential Hypertension ; Humans ; Hypertension ; Kidney ; pathology ; Magnetic Resonance Imaging

Relapse may occur in about 75% of patients who experienced the resection of hepatic metas-tases from coloreetal cancer. Perioperative chemotherapy with 5-fluorouraeil, leucovorin and oxaliplatin (FOLFOX) can reduce the risk of disease progression in eligible and reseeted patients compared with surgery a-lone. Pre-operative chemotherapy may cause vascular changes and steatohepatitis, which has the potential to in-crease the risks of surgery. A pooled analysis shows a trend of a longer median progression-free survival dura-tion among pest-operative patients who received adjuvant chemotherapy with 5-fluorouraeil and leueovorin-based regimen. Bevaeizumab in either pre-or pest-operative chemotherapy does not increase surgical complications.How to choose the best time and duration of the ebemotherapy needs to be further studied.

The antiEGFR monoclonal antibodies cetuximab and panitumumab have been proven to be efficient in MCRC. The degree of EGFR expression (as confirmed by immunohistochemical analysis) did not correhte with the clinical response. In this review, we describe the current status of markers that might identify patients who are likely to benefit from treatment with cetuximab or panitumumab. These molecular markers include KRAS mutations, EGFR copy number, EGFR ligands (EGF, epiregulin), cyclin DI, IgG FcγR (FCGR2A-HI31R and FCGR3A-V158F), and nuclear factor-κB, that are crucial to avoid anti-EGFR treatment toxicity and reduce treatment cost.