Program of Multiprofessional Health Residency, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil; Graduate Student of the Post-graduate Program in Physiotherapy, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil.
Post-graduate Program in Physiotherapy, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil.
J Bodyw Mov Ther. 2021 Oct;28:478-482. doi: 10.1016/j.jbmt.2021.07.040. Epub 2021 Aug 8.
Low back pain during pregnancy is very common and thermography seems to be a promising method of evaluation for pregnant women, because it is painless and safe. The aim of the present study was to evaluate low back pain, during pregnancy, using thermography together with artificial intelligence.
A cross-sectional study was carried out with pregnant women recruited from a university hospital. The following data were collected: (a) clinical data; (b) physical assessment with mobility and low back pain provocation tests; and (c) thermograms acquisitions, in a controlled environment. Artificial intelligence and the statistical tests were used to compare the groups' mean: with low back pain (LBP) and without low back pain (WLBP).
Thirty pregnant women took part, with fifteen in each group. The mean ± Standard Deviation temperature of the lumbar region in both groups were 32.7 ± 1.05 °C and 32.6 ± 1.01 °C for LBP and WLBP, respectively. There was not any difference in temperature between the groups; however, the artificial intelligence software found thermogram differences between groups; furthermore, the correlation between pain intensity and functionality was found.
Thermography associated with artificial intelligence analyses demonstrated to be a promising method as an adjunct to clinical evaluation.
孕期腰痛非常常见，热成像似乎是评估孕妇的一种很有前途的方法，因为它无痛且安全。本研究的目的是使用热成像结合人工智能评估孕期腰痛。
这是一项横断面研究，研究对象为从大学医院招募的孕妇。收集了以下数据：（a）临床数据；（b）采用活动度和腰痛诱发试验进行的体格评估；以及（c）在受控环境中采集热图像。使用人工智能和统计检验比较有腰痛（LBP）和无腰痛（WLBP）两组的平均数值。
共有 30 名孕妇参与，每组 15 名。两组的腰椎区平均温度分别为 32.7±1.05°C 和 32.6±1.01°C。两组之间的温度没有差异；然而，人工智能软件发现了两组之间的热图像差异；此外，还发现了疼痛强度和功能之间的相关性。
热成像结合人工智能分析似乎是一种很有前途的方法，可以作为临床评估的辅助手段。
