Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.

结核病 (TB) 是 HIV 阳性患者可预防死亡的主要原因，但往往未被诊断和治疗。胸部 X 光检查通常用于辅助诊断，但由于放射学表现不典型以及在合并感染最常见的地区放射科医生短缺，这带来了额外的挑战。我们开发了一种深度学习算法，利用来自南非两家医院的 677 名疑似结核病 HIV 阳性患者的临床信息和胸部 X 光图像来诊断结核病。然后，我们试图确定该算法是否可以作为基于网络的诊断助手帮助临床医生诊断 HIV 阳性患者的结核病。该算法的使用使临床医生的准​​确性得到了适度但具有统计学意义的改善 ( p  = 0.002)，将临床医生的平均准确性从无帮助的 0.60 (95% CI 0.57, 0.63) 提高到有帮助的 0.65 (95% CI 0.60, 0.70) 。然而，辅助临床医生的准​​确性显着低于 独立算法（ p < 0.001），独立算法在相同的未见过的测试用例上的准确性为 0.79（95% CI 0.77，0.82）。这些结果表明，深度学习辅助可以提高临床医生使用胸部 X 光进行结核病诊断的准确性，这对于 HIV/结核病双重感染负担较高的环境非常有价值。此外，独立算法的高精度表明了潜在的价值，特别是在缺乏放射学专业知识的环境中。