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中华脑血管病杂志(电子版) ›› 2024, Vol. 18 ›› Issue (06) : 528 -534. doi: 10.11817/j.issn.1673-9248.2024.06.002

论著

人工智能辅助口腔环境管理模式在颈动脉狭窄合并牙周炎患者中的应用研究
周艳1, 赵梦扬1, 乔彤1, 蔡颖1,()   
  1. 1.210008 南京大学医学院附属鼓楼医院血管外科
  • 收稿日期:2024-01-08 出版日期:2024-12-01
  • 通信作者: 蔡颖
  • 基金资助:
    国家自然科学基金资助项目(81870348)

Application of artificial intelligence-assisted oral environment management model in patients with carotid stenosis and periodontitis

Yan Zhou1, Mengyang Zhao1, Tong Qiao1, Ying Cai1,()   

  1. 1.Department of Vascular Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008,China
  • Received:2024-01-08 Published:2024-12-01
  • Corresponding author: Ying Cai
引用本文:

周艳, 赵梦扬, 乔彤, 蔡颖. 人工智能辅助口腔环境管理模式在颈动脉狭窄合并牙周炎患者中的应用研究[J/OL]. 中华脑血管病杂志(电子版), 2024, 18(06): 528-534.

Yan Zhou, Mengyang Zhao, Tong Qiao, Ying Cai. Application of artificial intelligence-assisted oral environment management model in patients with carotid stenosis and periodontitis[J/OL]. Chinese Journal of Cerebrovascular Diseases(Electronic Edition), 2024, 18(06): 528-534.

目的

探讨利用人工智能辅助口腔环境管理模式在颈动脉狭窄合并牙周炎患者中的应用价值。

方法

选取2022 年1 月至2023 年5 月在南京鼓楼医院血管外科住院的颈动脉狭窄合并牙周炎的患者,利用人工智能识别斑块不稳定患者共80 例,随机分为干预组和对照组,每组各40 例。对照组实施健康教育模式,干预组在健康教育模式基础上实施口腔环境管理模式。采用重复测量方差分析比较不同时间点2 组入院时,出院前,出院后3、6 个月口腔健康素养量表得分、Beck 口腔评分、牙菌斑指数;采用秩和检验比较2 组术后3 d 炎症指标变化;采用连续矫正χ2 检验比较2 组出院后3、6 个月再狭窄及卒中发生率的差异。

结果

经重复测量方差分析,干预组和对照组口腔健康素养量表得分在不同时间点、组别与时间点的交互作用比较差异有统计学意义(P<0.05),出院后6 个月组间比较差异具有统计学意义[(172.35±27.73)分 vs(160.17±23.36)分;t=-2.124,P=0.037]。2 组患者Beck 口腔评分不同时间点、组别与时间点的交互作用比较差异有统计学意义(P<0.05),出院后3、6 个月组间比较差异具有统计学意义[(7.58±1.32)分 vs(8.43±1.50)分,t=-2.692、P=0.009;(6.43±1.28)vs(7.28±1.36)分,t=-2.881、P=0.005]。2 组患者牙菌斑指数在不同组别、不同时间点比较差异有统计学意义(P<0.05),出院后6 个月组间比较差异有统计学意义(1.60±0.67 vs 1.95±0.50,t=-2.636,P=0.010)。口腔健康素养量表得分、Beck 口腔评分及牙菌斑指数的组内比较:出院后6 个月与入院时、出院时、出院后3 个月比较,差异均有统计学意义(P 均<0.05)。术后3 d,2 组白细胞介素-6、中性粒细胞计数比较[3.24(2.09,5.60)pg/ml vs 6.65(4.70,8.50)pg/ml;3.60(3.20,4.00)×109/L vs 5.25(4.40,7.50)×109/L],差异具有统计学意义(Z=2.990、P=0.003;Z=-4.266、P=0.001)。出院后6 个月2 组并发症发生率比较[0(0.00%) vs 6(15.00%)],差异具有统计学意义(χ2=4.505,P=0.034)。

结论

利用人工智能识别斑块不稳定患者,针对性实施口腔环境管理模式可有效减少牙菌斑,改善患者口腔状况并提升口腔健康素养水平,降低患者术后感染及并发症的发生率。

Objective

To explore the application value of artificial intelligence-assisted oral environment management mode in patients with carotid artery stenosis and periodontitis.

Methods

80 patients were enrolled with carotid artery stenosis and periodontitis hospitalized in the vascular surgery department of Nanjing Drum Tower Hospital from January 2022 to May 2023.Using artificial intelligence to identify unstable plaques, they were randomly assigned to either an intervention group or a control group, each with 40 cases.The control group implemented a standard health education model, while the intervention group implemented an oral environment management model based on the health education model.We employed repeated measures ANOVA to compare the scores of the Oral Health Literacy Scale, Beck Oral Score, and Plaque Index between the two groups at different time points: admission, pre-discharge, and at 3 and 6 months post-discharge; The rank sum test was used to compare the changes in inflammatory markers between the two groups 3 days post-surgery, and the χ2 test was used to compare the incidence of restenosis and stroke between the two groups at 3 and 6 months post-discharge.

Results

Repeated measures ANOVA revealed statistically significant difference in the Oral Health Literacy Scale scores between the intervention group and the control group at various time points, groups, and time points (P<0.05).The difference between the groups at 6 months post-discharge was statistically significant [(172.35±27.73) points vs (160.17±23.36) points;t=-2.124, P=0.037].Beck Oral Score also showed significant differences at different time points, groups, and time points (P<0.05), with significant differences at 3 and 6 months post-discharge [(7.58±1.32) points vs(8.43±1.50) points], t=-2.692, P=0.009; (6.43±1.28) points vs (7.28±1.36) points, t=-2.881, P=0.005].Plaque Index differences were significant at various groups and time points (P<0.05), with a significant difference at 6 months post-discharge (1.60±0.67 vs 1.95±0.50, t=-2.636, P=0.010).Intragroup comparison of the scores of the Oral Health Literacy Scale, Beck Oral Score, and Plaque Index showed statistically significant differences at 6 months post-discharge compared to admission, discharge, and 3 months postdischarge (all P<0.05).Interleukin-6 and neutrophil counts 3 days post-surgery were significantly different between the two groups [3.24 (2.09, 5.60) pg/ml vs 6.65 (4.70, 8.50) pg/ml; 3.60 (3.20, 4.00)×109/L vs 5.25(4.40, 7.50)×109/L], with significant differences (Z=2.990, P=0.003; Z=-4.266, P=0.001).The incidence of complications at 6 months post-discharge was significantly different between the groups [0 (0.00%) vs 6(15.00%)], with significant differences (χ2=4.505, P=0.034).

Conclusion

Artificial intelligence-assisted identification of patients with unstable plaques, coupled with targeted oral environment management, can effectively reduce plaque, improve oral health, enhance oral health literacy, and reduce the incidence of postoperative infections and complications.

表1 2 组行颈动脉内膜切除术的颈动脉狭窄合并牙周炎患者基线临床资料对比
表2 2 组行颈动脉内膜切除术的颈动脉狭窄合并牙周炎患者口腔评估结果比较(分,±s
表3 2 组行颈动脉内膜切除术的颈动脉狭窄合并牙周炎患者炎症指标比较[±s/MQR)]
表4 2 组行颈动脉内膜切除术的颈动脉狭窄合并牙周炎患者术后并发症的比较[例(%)]
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