The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% (P<0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% (P≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

中文翻译：

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人工智能帮助病理学家提高乳腺癌淋巴结转移检测的诊断准确性和效率。


淋巴结转移的检测对于乳腺癌分期至关重要，尽管这是一项繁琐且耗时的任务，而且病理学家的敏感性并不理想。人工智能 (AI) 可以帮助病理学家检测淋巴结转移，这有助于减轻工作量问题。我们研究了病理学家在人工智能的帮助下表现如何变化。使用超过 32000 个乳腺前哨淋巴结全切片图像 (WSI) 与来自 8000 多名患者的相应病理报告相匹配来训练 AI 算法。该算法突出显示了可疑转移的区域。三名病理学家被要求审查包含 167 个乳腺前哨淋巴结 WSI 的数据集，其中 69 个含有不同大小的癌症转移，丰富了具有挑战性的病例。九十八张载玻片是良性的。病理学家在有或没有人工智能辅助的情况下以数字方式读取数据集两次，随机化载玻片和读取顺序以减少偏差，中间间隔 3 周的清洗期。他们的幻灯片诊断被记录下来，并在阅读期间被计时。无辅助阶段每张幻灯片的平均读取时间为 129 秒，而 AI 辅助阶段为 58 秒，总体效率提高了 55%（P<0.001）。这些效率提升适用于良性和恶性 WSI。 3 名阅读病理学家中的 2 名敏感性显着提高，从 74.5% 提高到 93.5% (P≤0.006)。这项研究强调，人工智能可以帮助病理学家将阅读时间缩短一半以上，并提高转移检出率。