Wirries André, Geiger Florian, Hammad Ahmed, Redder Andreas, Oberkircher Ludwig, Ruchholtz Steffen, Bluemcke Ingmar, Jabari Samir
Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany.
Center for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, Germany.
Diagnostics (Basel). 2021 Oct 20;11(11):1934. doi: 10.3390/diagnostics11111934.
Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.
背痛患者很常见，由于可能的病因众多以及治疗的个体差异，在日常医疗实践中构成了一项挑战。借助人工智能预测病因和治疗效果可以改善并简化治疗。在一个由1000名接受保守治疗的背痛患者组成的示例性群体中，研究了通过结合不同的人工智能方法是否能够预测治疗效果及潜在诊断。为此，分析了监督式和非监督式人工智能方法，并开发了一种结合预测结果的方法。监督式人工智能适用于在最小临床差异边界处预测治疗效果。非监督式人工智能可以显示数据集中的模式。我们可以表明，只有通过结合不同的人工智能方法和基线数据，才有可能识别潜在的诊断组。当单独的人工智能方法仅产生微弱结果时，所提出的人工智能算法组合应用方法显示了一条在异构数据集中建立相关性的可转移途径。
