Bennasar Catalina, Nadal-Martínez Antonio, Arroyo Sebastiana, Gonzalez-Cid Yolanda, López-González Ángel Arturo, Tárraga Pedro Juan
Academia Dental de Mallorca (ADEMA), School of Dentistry, University of Balearic Islands, 07122 Palma de Mallorca, Spain.
Soft Computing, Image Processing and Aggregation (SCOPIA) Research Group, University of the Balearic Islands (UIB), 07122 Palma de Mallorca, Spain.
Diagnostics (Basel). 2025 Apr 16;15(8):1009. doi: 10.3390/diagnostics15081009.
: In a previous study, we utilized categorical variables and machine learning (ML) algorithms to predict the success of non-surgical root canal treatments (NSRCTs) in apical periodontitis (AP), classifying the outcome as either success (healed) or failure (not healed). Given the importance of radiographic imaging in diagnosis, the present study evaluates the efficacy of deep learning (DL) in predicting NSRCT outcomes using two-dimensional (2D) periapical radiographs, comparing its performance with ML models. : The DL model was trained and validated using leave-one-out cross-validation (LOOCV). Its output was incorporated into the set of categorical variables, and the ML study was reproduced using backward stepwise selection (BSS). The chi-square test was applied to assess the association between this new variable and NSRCT outcomes. Finally, after identifying the best-performing method from the ML study reproduction, statistical comparisons were conducted between this method, clinical professionals, and the image-based model using Fisher's exact test. : The association study yielded a -value of 0.000000127, highlighting the predictive capability of 2D radiographs. After incorporating the DL-based predictive variable, the ML algorithm that demonstrated the best performance was logistic regression (LR), differing from the previous study, where random forest (RF) was the top performer. When comparing the deep learning-logistic regression (DL-LR) model with the clinician's prognosis (DP), DL-LR showed superior performance with a statistically significant difference (-value < 0.05) in sensitivity, NPV, and accuracy. The same trend was observed in the DL vs. DP comparison. However, no statistically significant differences were found in the comparisons of RF vs. DL-LR, RF vs. DL, or DL vs. DL-LR. : The findings of this study suggest that image-based artificial intelligence models exhibit superior predictive capability compared with those relying exclusively on categorical data. Moreover, they outperform clinician prognosis.
在先前的一项研究中，我们利用分类变量和机器学习（ML）算法来预测根尖周炎（AP）中非手术根管治疗（NSRCT）的成功率，将结果分类为成功（愈合）或失败（未愈合）。鉴于根尖片成像在诊断中的重要性，本研究评估深度学习（DL）在使用二维（2D）根尖片预测NSRCT结果方面的有效性，并将其性能与ML模型进行比较。
DL模型使用留一法交叉验证（LOOCV）进行训练和验证。其输出被纳入分类变量集，并使用向后逐步选择（BSS）重现ML研究。应用卡方检验来评估这个新变量与NSRCT结果之间的关联。最后，在从ML研究重现中确定最佳性能方法后，使用Fisher精确检验对该方法、临床专业人员和基于图像的模型进行统计比较。
关联研究得出的P值为0.000000127，突出了2D根尖片的预测能力。纳入基于DL的预测变量后，表现最佳的ML算法是逻辑回归（LR），这与先前的研究不同，先前研究中随机森林（RF）是表现最佳的算法。将深度学习 - 逻辑回归（DL - LR）模型与临床医生的预后（DP）进行比较时，DL - LR在敏感性、阴性预测值和准确性方面表现出优越性能，差异具有统计学意义（P值<0.05）。在DL与DP的比较中也观察到相同趋势。然而，在RF与DL - LR、RF与DL或DL与DL - LR的比较中未发现统计学上的显著差异。
本研究结果表明，与仅依赖分类数据的模型相比，基于图像的人工智能模型具有优越的预测能力。此外，它们优于临床医生的预后判断。
