Li Haoyang, Zhou Juexiao, Zhou Yi, Chen Qiang, She Yangyang, Gao Feng, Xu Ying, Chen Jieyu, Gao Xin
Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China.
Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Front Physiol. 2021 Jun 22;12:655556. doi: 10.3389/fphys.2021.655556. eCollection 2021.
Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20-50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on data set, and 0.820 and 0.824, respectively on data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.
牙周炎在发达国家和发展中国家都是一种普遍且不可逆的慢性炎症性疾病，影响着全球约20%-50%的人口。自动诊断牙周炎的工具对于筛查牙周炎高危人群的需求很高，其早期检测可以预防牙齿脱落的发生，尤其是在牙科专业人员有限的当地社区和医疗机构中。在医学领域，医生需要理解并信任计算模型做出的决策，开发可解释的模型对于疾病诊断至关重要。基于这些考虑，我们提出了一种名为Deetal-Perio的可解释方法，用于预测牙科全景X光片中牙周炎的严重程度。在我们的方法中，牙槽骨吸收（ABL）作为牙周炎诊断的临床标志，可以被解释为关键特征。为了计算ABL，我们还提出了一种牙齿编号和分割的方法。首先，Deetal-Perio通过Mask R-CNN结合一种新颖的校准方法对单个牙齿进行分割和索引。接下来，Deetal-Perio分割牙槽骨的轮廓，并计算单个牙齿的比例来表示ABL。最后，Deetal-Perio根据所有牙齿的比例预测牙周炎的严重程度。在 数据集上，我们方法中牙周炎预测任务的宏F1分数和准确率分别达到0.894和0.896，在 数据集上分别为0.820和0.824。整个架构不仅优于现有方法，并且在牙周炎预测以及牙齿编号和分割任务的两个数据集上都表现出鲁棒性，而且对于医生来说是可解释的，以便他们理解Deetal-Perio表现出色的原因。
