PDF(Pubmed)
Abstract：
BACKGROUND: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one\'s own patient population, and how to operationalize such a model in practice.
METHODS: A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.
RESULTS: A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability.
CONCLUSIONS: Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.
METHODS: A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.
RESULTS: A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability.
CONCLUSIONS: Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.
摘要：
背景：在过去十年中，使用人工智能（AI）预测甲状腺结节诊断的临床结果呈指数级增长。最大的挑战是理解适用于自己患者群体的最佳模型，以及如何在实践中实施这样的模型。
方法：在2015年1月1日至2023年1月1日之间，对PubMed和IEEEXplore进行了文献检索，以研究使用AI的可疑甲状腺结节的诊断测试。我们排除了未经预期或外部验证的文章，非小学文学，重复项,专注于非结节性甲状腺疾病，不使用AI，以及那些偶然利用人工智能来支持标准临床实践之外的实验诊断的人。质量按牛津证据等级评定。
结果：共确定了61项研究；所有研究均进行了外部验证，16项研究是前瞻性的，33人将模型与医生对地面实况的预测进行了比较。50篇论文报道了统计验证。提取了一条诊断管道，产生五个高水平的结果：(1)结节定位，(2)超声风险评分,（3）分子状态，（4）恶性肿瘤，（5）长期预后。七项前瞻性研究验证了一个单一的商业人工智能;优势包括自动从超声结节特征评估和协助医生预测恶性肿瘤风险。而弱点包括自动边际预测和观察者间的变异性。
结论：模型主要使用超声图像来预测恶性肿瘤。在FDA批准的四种产品中，只有S-Detect被广泛验证。在本地实施AI模型需要数据清理和重新验证，以确保适当的临床表现。
方法：在2015年1月1日至2023年1月1日之间，对PubMed和IEEEXplore进行了文献检索，以研究使用AI的可疑甲状腺结节的诊断测试。我们排除了未经预期或外部验证的文章，非小学文学，重复项,专注于非结节性甲状腺疾病，不使用AI，以及那些偶然利用人工智能来支持标准临床实践之外的实验诊断的人。质量按牛津证据等级评定。
结果：共确定了61项研究；所有研究均进行了外部验证，16项研究是前瞻性的，33人将模型与医生对地面实况的预测进行了比较。50篇论文报道了统计验证。提取了一条诊断管道，产生五个高水平的结果：(1)结节定位，(2)超声风险评分,（3）分子状态，（4）恶性肿瘤，（5）长期预后。七项前瞻性研究验证了一个单一的商业人工智能;优势包括自动从超声结节特征评估和协助医生预测恶性肿瘤风险。而弱点包括自动边际预测和观察者间的变异性。
结论：模型主要使用超声图像来预测恶性肿瘤。在FDA批准的四种产品中，只有S-Detect被广泛验证。在本地实施AI模型需要数据清理和重新验证，以确保适当的临床表现。
