Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80–0.82), and specificity was 92% (95% CI, 0.92–0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.
卵巢癌是第六大常见恶性肿瘤，其十年期总体生存率为35%。超声检查广泛应用于卵巢肿瘤诊断，准确的术前诊断对患者个体化治疗至关重要。人工智能作为妇科领域的新兴技术，已被证实有助于卵巢癌的超声诊断。本研究通过检索Embase和MEDLINE数据库，筛选出所有在超声检查中应用人工智能诊断卵巢恶性肿瘤的原始临床研究，并以组织病理学结果为金标准纳入分析。对各研究的诊断效能进行独立分析后，采用合并评估方法进行整体评价。初步检索获得3726篇文献，其中63篇符合摘要筛选标准。最终纳入14项以组织病理学为金标准、应用人工智能进行卵巢恶性肿瘤超声诊断的研究，这些研究样本量各异且采用不同方法，共涉及15358张超声图像。合并分析显示总体敏感度为81%（95% CI 0.80-0.82），特异度为92%（95% CI 0.92-0.93），表明人工智能在卵巢癌超声诊断中具有良好的诊断效能。未来需要进一步开展前瞻性研究以验证人工智能在临床实践中的应用价值。
肿瘤(癌症)患者之家