An algorithmic approach to reducing unexplained pain disparities in underserved populations
An algorithmic approach to reducing unexplained pain disparities in underserved populations
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DOI：
10.1038/s41591-020-01192-7
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发表时间：
2021-01-01
期刊：
影响因子：
82.9
通讯作者：
Obermeyer, Ziad
中科院分区：
文献类型：
--
作者：
Pierson, Emma;Cutler, David M.;Obermeyer, Ziad
服务不足的人群经历更高程度的疼痛。即使在控制了骨关节炎等疾病的客观严重程度之后，这些差异仍然存在，正如人类医生使用医学图像所分级的那样，这增加了服务不足的患者的疼痛源于膝盖外部因素（如压力）的可能性。在这里，我们使用深度学习方法来测量骨关节炎的严重程度，通过使用膝关节X射线来预测患者经历的疼痛。我们表明，这种方法大大减少了无法解释的种族差异的痛苦。相对于放射科医生分级的严重程度标准测量，仅占疼痛种族差异的9%（95%置信区间（CI），3-16%），算法预测占差异的43%，或4.7倍（95% CI，3.2- 11.8倍），低收入和受教育程度较低的患者也有类似的结果。这表明，许多服务不足的患者的疼痛源于膝关节内的因素，而不是反映在标准的放射学措施的严重程度。我们表明，该算法的能力，以减少无法解释的差异是植根于种族和社会经济的多样性的训练集。由于算法严重性测量更好地捕捉服务不足的患者的疼痛，并且严重性测量影响治疗决策，因此算法预测可能会纠正获得关节成形术等治疗的差异。机器学习方法测量骨关节炎的严重疼痛应用于X-膝关节X线片提示，与使用标准X线片相比，疾病严重程度的衡量标准。
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7x more (95% CI, 3.2-11.8x), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity.