分化型甲状腺癌人工智能语音随访系统的构建及应用

Establishment and application of artificial intelligence voice follow-up system for differentiated thyroid cancer

  • 摘要:
    目的 构建分化型甲状腺癌人工智能语音随访系统,评价该系统信息采集的完整性和准确性。
    方法 根据分化型甲状腺癌患者术后用药及复查情况,构建分化型甲状腺癌人工智能语音随访系统。对2024年2月25—29日本院甲乳血管外科甲状腺癌数据库管理系统中未进行随访的2 471例分化型甲状腺癌患者进行电话随访,并随机抽取10%, 对220例患者的通话记录进行完整性和准确性分析。
    结果 本研究成功构建分化型甲状腺癌人工智能语音随访系统,总体电话接通率为88.9%。对于呼叫成功的患者,人工智能语音随访系统的随访平均用时(1.7±0.5) min, 总体信息采集完整率为83.8%, 术后3个月随访信息采集完整率为92.9%, 术后长期随访信息采集完整率为82.7%, 差异有统计学意义(χ2=15.200, P < 0.001)。该随访系统信息采集准确率为94.1%。说方言随访者和说普通话随访者的信息采集准确率分别为92.2%和95.4%, 差异无统计学意义(χ2=0.957, P=0.389)。
    结论 分化型甲状腺癌人工智能语音随访系统具有较高的信息采集效率、完整性和准确性, 可降低随访人力成本,在大规模分化型甲状腺癌人群随访中具有良好的应用前景。

     

    Abstract:
    Objective To establish an artificial intelligence voice follow-up system for differentiated thyroid cancer and evaluate the completeness and accuracy of information collection by this system.
    Methods Based on the postoperative medication and follow-up status of patients with differentiated thyroid cancer, an artificial intelligence voice follow-up system for differentiated thyroid cancer was established. From February 25 to 29, 2024, a total of 2, 471 differentiated thyroid cancer patients without follow-up in the thyroid cancer database management system of the Department of Thyroid and Breast Vascular Surgery of the Hospital were followed up by telephone, and 220 patients (approximately 10% of the total) were randomly selected for completeness and accuracy analyses of their call records.
    Results This study successfully established an artificial intelligence voice follow-up system for differentiated thyroid cancer, and the overall call connection rate was 88.9%. For patients with successful calls, the average follow-up time of the artificial intelligence voice follow-up system was (1.7±0.5) minutes, the overall information collection completeness rate was 83.8%, with a completeness rate of 92.9% for follow-up at 3 months after surgery and 82.7% for long-term follow-up, and the differences were statistically significant (χ2=15.200, P < 0.001). The accuracy rate of information collection by this follow-up system was 94.1%. The accuracy rates of information collection for patients who spoke dialect and Mandarin during follow-up were 92.2% and 95.4% respectively, and the difference was not statistically significant (χ2=0.957, P=0.389).
    Conclusion The artificial intelligence voice follow-up system for differentiated thyroid cancer has high efficiency, completeness, and accuracy in information collection, which can reduce human costs for follow-up and has good application prospects in large-scale follow-up of populations with differentiated thyroid cancer.

     

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