# Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones
## 4 Discussion  
Even though intraoral photos taken by smartphones are not usually used for clinical diagnosis of dental caries, the literature showed good accuracy of visual inspection by photographs in detecting dental caries [22]. The doctors' experience is also an important factor in diagnosing caries [28]. In the current study, the reference standard was the diagnosis of a single experienced dentist based on visual inspection by smartphone photos. Cavitated caries on the tooth surface are a definite sign of dental caries. Differentiating cavitated lesions from sound enamel surfaces on smartphone images is feasible and accurate [29]. In this study, the specificity and sensitivity for cavitated caries detection of the two better-performing models--YOLOv3 and Faster R-CNN--were about 70% and 90%, respectively. These data were lower than previous in vitro studies. For example, a study by [PERSON] et al. in 2021 showed a high result of sensitivity of 88.1% and specificity of 96.6% [16]. Two main factors led to the result. First, our study used intraoral photos taken via smartphones for deep learning training, which means there were several unfavored factors when taking intraoral photos that could interfere with the quality of photos, such as saliva, a lack of light, different camera angles, or presence of soft tissues rather than tooth exclusively. Secondly, the device taking all photos in the current study was a universal smartphone such as iPhone 7. Thus, different types of photos with divergent angles and low quality may lower the diagnostic specificity and sensitivity [30]. Initial caries lesions present as white or yellow-brown spots without destroying any structure or cavitated holes on the enamel surface [19]. Therefore, identification of the initial caries is challenging, even for experienced dentists. A meta-analysis showed the sensitivity and specificity of photographic visual examination for initial caries were only 67% and 79% [31]. In detecting VNC or initial caries, all four models in our study showed relatively low sensitivity, YOLOv3 (36.9%), Faster R-CNN (23.4%), Retina Net (26.5%), and SSD (0%). These deep learning models were trained based on the features that reflected the color, size, and location of predetermined lesions. Since the presence of initial lesions was vague and indistinctive, it might require a large size of data to obtain an accurate result. For example, the Faster R-CNN model misdiagnosed a reflection of light as a white-spot early caries (Figure 5). Our study was conducted with a modest number of clinical images, and a relatively low level of sensitivity in diagnosing initial caries was \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Deep** & \multirow{2}{*}{**Sensitivity \%**} & \multirow{2}{*}{**Specificity \%**} & \multirow{2}{*}{**Accuracy \%**} & \multirow{2}{*}{**Precision \%**} \\
**Learning** & & & & \\ \hline YOLOv3 & 74 & 86.6 & 83.4 & 65.3 \\ Faster R-CNN & 71.2 & 92.9 & 87.4 & 77.3 \\ RetinaNet & 63.2 & 89.8 & 83 & 67.7 \\ SSD & 26 & 99.7 & 81 & 97.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model evaluation: results of C vs. NC classification. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Deep** & \multirow{2}{*}{**Sensitivity \%**} & \multirow{2}{*}{**Specificity \%**} & \multirow{2}{*}{**Accuracy \%**} & \multirow{2}{*}{**Precision \%**} \\
**Learning** & & & & \\ \hline YOLOv3 & 36.9 & 71.4 & 60.7 & 49.7 \\ \hline Faster R-CNN & 23.4 & 87.7 & 67.8 & 61.5 \\ \hline RetinaNet & 26.5 & 83.3 & 65.7 & 61.3 \\ \hline SSD & 0 & 99.7 & 68.8 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model evaluation: results of VNC vs. NSC classification. not unexpected. Furthermore, dental caries detection from intraoral photos taken using smartphones is a distinctive small-object detection problem. This problem has severely affected the diagnostic results of deep learning algorithms. The limitation of the results of the present study prompts us to make extensive developments for these algorithms in near future.