lity study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay. METHODS: A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation–derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay. RESULTS: A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and −0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model–derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01–1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0–1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96–0.99]; P < .05). CONCLUSIONS: In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research....

中文翻译：

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使用深度图像分割和相关术后结果对中低收入国家术中纸质健康记录中符号表示的血压数据进行数字化：一项可行性研究

lity 研究，我们的目标是：(1) 确定用于从术中 PHR 图像中数字化手绘血压符号的 U-Net 深度图像分割方法的检测性能和预测误差，以及 (2) 评估关联深度图像分割衍生的血压参数与术后死亡率和住院时间之间的关系。方法：我们团队开发的智能手机 mHealth 平台用于捕获已完成的术中 PHR 的图像。使用 2 阶段深度图像分割建模方法为收缩压 (SBP) 和舒张压 (DBP) 创建 2 个单独的分割掩模。利用迭代后处理将分割掩码结果转换为数字 SBP 和 DBP 值。通过与地面真实值进行比较，对 U-Net 模型的检测性能和预测误差进行了评估。使用多元回归分析，我们研究了深度图像分割衍生的血压值、在预定血压范围内花费的总时间以及术后结果（包括院内死亡率和住院时间）之间的关联。结果：手术后对总共 350 个术中 PHR 进行了成像。SBP 和 DBP 符号检测的总体准确度分别为 0.839 和 0.911。SBP 和 DBP 的实际血压值和预测血压值之差的平均误差率和标准差分别为 2.1 ± 4.9 和 -0.8 ± 3.9 mm Hg。使用 U-Net 模型导出的血压，DBP <50 mm Hg 的时间分钟数（比值比 [OR]，1.03；CI，1.01–1.05；P = . 003) 与院内死亡率增加有关。此外，收缩压在 80 至 90 毫米汞柱之间的累积时间增加与住院时间延长显着相关（发生率比为 1.02 [1.0–1.03]；P < .05），而累积时间增加显着SBP 在 140 至 160 mm Hg 之间与较短的住院时间相关（发病率比为 0.9 [0.96–0.99]；P < .05）。结论：在这项研究中，我们报告了我们使用深度图像分割模型对术中麻醉 PHR 中符号表示的血压进行数字化的经验。我们的数据支持进一步开发这种将 LMIC 的 PHR 数字化的新方法，为基于质量改进和基于结果的研究提供可访问、精选和可重现的数据……