da Silva Sara Maria Santos Dias, Ferreira Camila Lopes, Rizzato Jaqueline Maria Brandão, Toledo Giovana Dos Santos, Furukawa Monique, Rovai Emanuel Silva, Nogueira Marcelo Saito, Carvalho Luis Felipe das Chagas E Silva de
Science Health Post-graduate Program, University of Taubaté - UNITAU, SP, Brazil.
Department of Diagnosis and Surgery, Institute of Science and Technology of São José dos Campos, Universidade Estadual Paulista (Unesp), São José Dos Campos, SP, Brazil.
Photodiagnosis Photodyn Ther. 2024 Apr;46:104106. doi: 10.1016/j.pdpdt.2024.104106. Epub 2024 Apr 25.
FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool.
Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP).
Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm, followed by data preprocessing and analysis using machine learning tools.
Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased.
The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.
傅里叶变换红外光谱（FT-IR）是一种重要且新兴的工具，可提供与生物流体生化组成相关的信息。证明其在区分健康组和疾病组方面具有有效性，有助于确立FT-IR作为快速筛查工具的用途，这一点很重要。
通过唾液诊断评估FT-IR与机器学习模型相结合对健康（对照组）、糖尿病（D组）和牙周炎（P组）患者以及两种疾病并存（DP组）进行分类的准确性，以及两种疾病之间的关联。
招募80例通过传统方法诊断为糖尿病和牙周炎的患者，并将其分配到四组中的一组。从每组的参与者（n = 20）中采集唾液样本，并使用布鲁克Alpha II光谱仪在600至1800 cm的FT-IR光谱指纹区域进行处理，随后使用机器学习工具进行数据预处理和分析。
可检测到各种FTI-R峰，并将其归因于特定的振动模式，这些振动模式根据配对组中显示的混淆矩阵进行分类。最高真阳性率（TPR）出现在C组与D组之间（93.5%±2.7%）、C组与DP组之间（89.2%±4.1%）以及D组和P组之间（90.4%±3.2%）。然而，P组与DP组中DP组的TPR更高（84.1%±3.1%），而D组与DP组中DP组的最高率为81.7%±4.3%。综合分析所有组，TPR降低。
所使用的系统便携且坚固，可作为一种新的诊断技术在临床环境和医院中广泛使用。我们团队正在进行研究，以巩固和扩展数据分析方法，使其对医疗保健专业人员来说语言友好。有可能以78%至93%的准确率对健康患者进行分类。观察到牙周炎和糖尿病之间的准确率超过80%。与成对分类相比，具有较低TPR的一般分类模型仅在没有关于糖尿病和牙周炎状态的患者先验信息的情况下才具有优势。
