OBJECTIVE: To review the research progress and challenges of poly (L-lactic acid) (PLLA) membrane in preventing tendon adhesion. METHODS: The relevant literature at home and abroad in recent years was extensively searched, covering the mechanism of tendon adhesion formation, the adaptation challenge and balancing strategy of PLLA, the physicochemical modification of PLLA anti-adhesion membrane and its application in tendon anti-adhesion. In this paper, the research progress and modification strategies of PLLA membranes were systematically reviewed from the three dimensions of tissue adaptation, mechanical adaptation, and degradation adaptation. RESULTS: The three-dimensional adaptation of PLLA membrane is optimized by combining materials (such as hydroxyapatite, polycaprolactone), structural design (multilayer/gradient membrane), and drug loading (anti-inflammatory drug). The balance between anti-adhesion and pro-healing is achieved, the mechanical adaptation significantly improve, and degradation is achieved (targeting the degradation cycle to 2-4 weeks to cover the tendon repair period). CONCLUSION In the future, it is necessary to identify the optimal balance point of three-dimensional fitness, unify the evaluation criteria and solve the degradation side effects through the co-design of physicochemical modification and drug loading system to break through the bottleneck of clinical translation.
Tissue Adhesions/prevention & control* ; Polyesters/chemistry* ; Humans ; Biocompatible Materials/chemistry* ; Tendons/surgery* ; Membranes, Artificial ; Tendon Injuries/surgery* ; Wound Healing ; Animals ; Durapatite/chemistry*

Tuberous sclerosis complex（TSC）is an autosomal dominant genetic disorder characterized by benign tumors in multiple systems，with prenatal involvement primarily affecting the heart and nervous system. Its incidence is approximately 1/6 000 - 1/10 000. The pregnant woman was 36-year-old with 26-week pregnancy and no history of hereditary diseases，underwent fetal echocardiography at Shenzhen Luohu People's Hospital. At 21 weeks' gestation，ultrasound showed fetal left atrial mass，suggesting cardiac myxoma. At 23 weeks' gestation，ultrasound showed multiple hyperechoic nodules in the ventricles，suggesting Tuberous sclerosis.Amniocentesis was subsequently performed. At 26 weeks' gestation，multiple intracranial nodules appeared，suggestive of Tuberous sclerosis complex. Finally，genetic testing of amniocentesis revealed heterozygous mutation of c.5228 G>A in the TSC2 gene of the tested individual，which was not carried by parents. The woman ultimately terminate the pregnancy. The author reviews this case and in conjunction with the literature，analyzes the prenatal ultrasonographic features of this disease，in order to remind sonographers to effectively improve the diagnosis of TSC，thereby reducing the rate of missed and misdiagnoses.

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 apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Radiation-induced esophageal injury (RIEI) is a frequent complication following radiotherapy for thoracic and head-neck malignancies, which may lead to severe sequelae including esophageal stricture and perforation, adversely affecting patients' quality of life and therapeutic outcomes. With advancements in radiotherapy techniques — particularly the adoption of unconventional fractionation regimens, concurrent chemoradiotherapy, and combined molecular targeted / immunotherapy — the incidence of RIEI has been increasing. In this review, recent advances in understanding the pathogenesis, clinical manifestations, risk factors, and management strategies for RIEI were comprehensively summarized. Current therapeutic approaches have evolved beyond conventional anti-inflammatory and nutritional support to include novel interventions such as targeted therapy, free radical scavengers, and microbiota modulation, etc. Future research should prioritize the development of optimized, individualized prevention and treatment protocols to mitigate RIEI risk and improve patient prognosis.

Tuberous sclerosis complex（TSC）is an autosomal dominant genetic disorder characterized by benign tumors in multiple systems，with prenatal involvement primarily affecting the heart and nervous system. Its incidence is approximately 1/6 000 - 1/10 000. The pregnant woman was 36-year-old with 26-week pregnancy and no history of hereditary diseases，underwent fetal echocardiography at Shenzhen Luohu People's Hospital. At 21 weeks' gestation，ultrasound showed fetal left atrial mass，suggesting cardiac myxoma. At 23 weeks' gestation，ultrasound showed multiple hyperechoic nodules in the ventricles，suggesting Tuberous sclerosis.Amniocentesis was subsequently performed. At 26 weeks' gestation，multiple intracranial nodules appeared，suggestive of Tuberous sclerosis complex. Finally，genetic testing of amniocentesis revealed heterozygous mutation of c.5228 G>A in the TSC2 gene of the tested individual，which was not carried by parents. The woman ultimately terminate the pregnancy. The author reviews this case and in conjunction with the literature，analyzes the prenatal ultrasonographic features of this disease，in order to remind sonographers to effectively improve the diagnosis of TSC，thereby reducing the rate of missed and misdiagnoses.

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 apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Radiation-induced esophageal injury (RIEI) is a frequent complication following radiotherapy for thoracic and head-neck malignancies, which may lead to severe sequelae including esophageal stricture and perforation, adversely affecting patients' quality of life and therapeutic outcomes. With advancements in radiotherapy techniques — particularly the adoption of unconventional fractionation regimens, concurrent chemoradiotherapy, and combined molecular targeted / immunotherapy — the incidence of RIEI has been increasing. In this review, recent advances in understanding the pathogenesis, clinical manifestations, risk factors, and management strategies for RIEI were comprehensively summarized. Current therapeutic approaches have evolved beyond conventional anti-inflammatory and nutritional support to include novel interventions such as targeted therapy, free radical scavengers, and microbiota modulation, etc. Future research should prioritize the development of optimized, individualized prevention and treatment protocols to mitigate RIEI risk and improve patient prognosis.

Objective:To explore the feasibility of extracting the key plane of the normal fetal palate on the 11-13 + 6 week from tomography ultrasonography imaging based on artificial intelligence. Methods:The fetal volume datas of 235 cases of 11-13 + 6 week normal fetal were collected from the Department of Ultrasound in the Luohu District People′s Hospital of Shenzhen and Huazhong University of Science and Technology Union Shenzhen Hospital from May 2020 to April 2021. The data acquisition was completed by sonographers A and B by using the GE Voluson E10 color Doppler ultrasound diagnostic instrument. All datas were marked offline by sonographer C. Tomographic imaging was performed on all included data by sonographer D, the tomographic images were saved and the time-consuming was recorded, and the datas of the sonographer group were obtained. The labeled data were randomly divided into the training set and test set for model transfer learning and testing.The 4-fold cross-validation was adopted to record the test set image output by the model and the time consumption to obtain the intelligent group data. A senior sonographer performed image analysis on the two groups of data images. The feasibility of the intelligent model was verified by comparing the score of the plane of retronasal triangle(RTP), the acquisition rate of RTP, the acquisition rate of the fault, and the time-consuming difference between the sonographer group and the intelligent group. Results:①There was no significant difference in the overall distribution of RTP scores between the sonographer group and intelligent group [5 (5, 6) points vs 5 (5, 6) points, Z=0.355, P=0.722]. The RTP acquisition rate of the sonographer group and intelligent group was not statistically significant (78.72% vs 76.60%, χ 2=0.55, P=0.458). The consistency and correlation of RTP obtained by the two groups were high (Kappa=0.645, φ=0.646, both P<0.001). ②The effective layers of the sonographer group were 9 (8, 9) and the intelligent group was 8 (7, 9). The fault acquisition rate of the doctor group was higher than that of the intelligent group (78.72% vs 68.51%, χ 2=12.52, P=0.001). The consistency and correlation of the two groups in obtaining faults were media (Kappa=0.503, φ=0.521, both P<0.001). ③The time-consuming of the intelligent group was significantly lower than that of the sonographer group [1.50 (1.23, 1.75)s vs 26.94 (22.28, 30.48)s, Z=11.440, P<0.001]. Conclusions:This research model can quickly and accurately realize the extraction and tomography of the key plane of the normal fetal palate on the 11-13 + 6 week.