# Modified Gingival Index (MGI) Classification Using Dental Selfies
## 4 Discussion  
This study is the first to our knowledge that utilized an mHealth app (iGAM) to study the use of dental selfies to reduce gingivitis in the home setting. We present a new method capable of accurately classifying gum condition based on the MGI score. From the results presented it is evident that despite the small dataset our method produced accurate classification of gum condition. Our method can aid the diagnostic process, towards prognosis by automatically reporting whether gums are healthy or inflamed, based on dental selfies taken by the user, using an efficient and focused set of features. Among the classifiers considered in our testing, XGBoost was most accurate for the most cases overall (6/8 positions at the first stage of the cascade and another 3/8 at the second stage). This is not surprising, considering XGBoost has proven accurate and efficient in many contexts [32]. Moreover, XGBoost is  
Figure 10: Feature contribution at the second stage of the cascade (mild/severe inflammation). The histogram represents the five most contributing features for the most accurate classifier for each of the eight tooth-positions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Tooth Number** & **AUC Score** \\ \hline
12 & 0.87 \\
11 & 0.86 \\
21 & 0.94 \\
22 & 0.91 \\
42 & 0.84 \\
41 & 1.0 \\
31 & 1.0 \\
32 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Second stage of cascade; test-set performance: for each tooth position, the AUC score for the classifier-features combination selected is reported. computationally relatively light, making it ideal for implementation on mobile devices. MLP proved most accurate for four cases. Our method uses a classifier per tooth per stage. The results of our lower teeth classifiers achieved higher accuracy than those for the upper teeth. In addition, lateral incisor classifiers were more accurate than central incisor classifiers. These results demonstrate the reliability of our method, since they correlate with clinical dental findings that the gums of the lower teeth tend to show inflammation and escalation first, due to proximity to the salivary glands and poor brushing technique [33]. As to the feature selection, we focused on a set of 35 features extracted per image, chosen specifically with the characteristics of our dental tooth images in mind (wide variability in lighting, perspective, cameras, and other qualities, with underlying similarity in content). The analysis by AutoML-H2O found the most contributing features for each of the classifiers (see Figures 9 and 10). Note that our innovative feature tailored to the dental context--TN (IM)--was selected by AutoML-H2O for the most cases 5/8 positions at the first stage, and 7/8 at the second stage, proving its value. Despite the two-stage cascade, note that at the second stage, our accuracy for three of the four lower teeth--which as we mentioned are the more significant ones--is 1.0 (see Table 5 and Figure 12) which means the accuracy of the model overall is dependent on the accuracy of the first stage. Looking at the Confusion Matrices in Table 12 teeth positions #11 and #22 have an overall accuracy of 0.9, but have an accuracy of 0 for healthy gums--meaning the system is oversensitive. This can be considered an advantag--false positives rather than false negatives, so as not to miss any images where gums are inflamed, we prefer a dentist to have to unnecessarily inspect healthy gums, than that they might miss a case of inflammation. The limitation for this research was the small dataset. We believe that the dataset size was a central reason we needed to train per-case classifiers. Since the attempt to train a single classifier for all teeth positions was unsuccessful, we trained a unique classifier-features selection per position.