BACKGROUND: There is yet no evidence about whether internal carotid artery stenting with cerebral protection devices is beneficial to reducing neurological complications. OBJECTIVE: To explore the safety and efficacy of carotid angioplasty and stenting with cerebral protection devices for carotid stenosis. DESIGN, TIME AND SETTING: Non-randomized concurrent control trial was performed at Hospital Affiliated to Medical College of Chinese People’s Armed Police Force from June 2005 to January 2007. PARTICIPANTS: Seventy-four patients with carotid artery stenosis underwent stenting, including 21 with cerebral protection devices (16 males and 5 females; average age of 66.4 years, range 50-79 years), and 53 with no protection devices (36 males and 17 females; average age of 69.2 years, range 52-83 years). METHODS: Size of cerebral protection devices was confirmed according to the diameter of normal vessel at distal carotid artery stenosis. The guide wire was sent into distal stenosis under guidance of pathway picture followed by cerebral protection device release. The stent passed over the stenosis and released to appropriate site. The protection device was removed when the stenosis was relieved confirmed by routine angiography. MAIN OUTCOME MEASURES: Features of stenting process; frequency of stroke attack perioperatively and during 12-month follow-up. All of them took periprocedual anticoagulation treatment, cerebral vascular angiograpgy. RESULTS: Seventy-six self-expandable stents were delivered in 74 patients with carotid stenosis. Twenty-one cerebral protection devices were employed including 8 Angioguard and 13 Filterwire. The patients without cerebral protection devices were predilated 20 times (37.7%) with the balloons, and all were postdilated; 3 cases (5.6%) developed brief decreased heart rate and hypotension after stent release. The patients with cerebral protection devices were predilated 6 times (28.5%) with balloons, and all were postdilated; 2 cased (9.5%) developed brief decreased heart rate and hypotension after stent release and 2 (9.5%) developed angiospasm. One patient (4.7%) with cerebral protection devices had cerebral infarction (4.7%) perioperatively and another had cerebral infarction (4.7%) during the follow up. While four patients in the group without cerebral protection devices had cerebral infarction (7.5%) perioperatively, and five had cerebral infarction (9.4%) during the follow up. There were no significant differences between two groups. CONCLUSION: The results of the study show that cerebral protection devices are not helpful to reduce neurological complications in patients with carotid artery stenosis after stenting.

Objective:To explore the feasibility of the AI intelligent reconstruction model based on knee joint magnetic resonance data developed by Nuctech Company Limited for evaluating knee cartilage injury.Methods:Thirty-three patients (a total of forty-one knees) who were hospitalized with severe knee osteoarthritis in Beijing Tsinghua Changgung Hospital from May 2021 to April 2022 were selected. All of them were planned to be performed total knee arthroplasty (TKA) for the treatment of knee osteoarthritis. Fifteen males with an average age of 71±5 years old and twenty six females with an average age of 71±9 years old were included in this study. There were 19 cases of left knee and 22 cases of right knee. Thin layer MRI examination on the patients' knee joints was performed before the surgery, and artificial intelligence model based on the thin layer MRI data of the knee joint was reconstructed. The cartilage part of the model was selected and corrected by Principal Component Analysis (PCA) in order to realize model straightening. The tibial plateau cartilage of knee joint which intercepted during operation was classified according to the International Cartilage Repair Society (ICRS). Finally the results were compared with the ICRS classification results of knee artificial intelligence reconstruction model and artificial recognition of knee joint MRI images.Results:Compared with the grade of cartilage injury intercepted during our operation which was according to the ICRS classification, the sensitivity of artificial intelligence reconstruction model for the diagnosis of cartilage injury with ICRS classification grade four was 93.1%. The specificity of artificial intelligence reconstruction model was 91.4%. The positive predictive value (PPV) of artificial intelligence reconstruction model was 92.2%. And the negative predictive value (NPV) of artificial intelligence reconstruction model was 80.3%. The area under ROC curve (AUC) was 0.92. The ICRS classification consistency between artificial intelligence model and physical inspection results was good with kappa value 0.81 ( P<0.001) . In the aspect of artificial recognition of cartilage injury grading in MRI images, the sensitivity of artificial recognition was 92.10% compared with the manual identification of cartilage injury classification in MRI images. The specificity of artificial recognition was 91.60%. The positive predictive value (PPV) of artificial recognition was 97.20% and the negative predictive value (NPV) of artificial recognition was 78.8%. The kappa value of the cartilage injury classification in MRI images consistency between artificial recognition and manual identification was 0.79 ( P<0.001). Conclusion:Based on the evaluation of cartilage injury by AI reconstruction model of knee joint, the sensitivity and specificity of the diagnosis of ICRS grade IV cartilage injury can be acceptable, but still needs to be improved.