# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI-Based Systems in Glaucoma
#### 4.3.2 Diagnosis of Glaucoma  
Glaucoma diagnostic methods, be they mono- or multi-modal, are usually utilized to yield a second judgement from ophthalmologists. Certain autonomous AI-based diagnostic systems can work without the involvement of clinical physicians, such as the IDx-DR platform for the detection of diabetic retinopathy [81].  
_Data-driven monomodal diagnosis_  
Most AI-based glaucoma diagnostic models use retinal imaging data because it provides rich information, including optic nerve color, texture, and morphology. Many traditional methodologies have used image feature extraction to train classification models. DL models continue to benefit from structural details in images, without feature engineering, and are the latest generation in glaucoma diagnosis. For instance, VGG and ResNet models are two of the most popular neural networks for structural data analysis of images.  
A number of selected diagnostic models are presented in Table 4.  
_Data-driven multimodal diagnosis_  
As demonstrated in an early study by [PERSON] et al., when the same network model was applied, i.e., back-propagation neural network (BPNN) on monomodal data (functional: VF; structural: OD/RNFL (retinal nerve fiber layer) measurements) and on multimodal data, BPNN performed better on multimodal data than on functional or structural data alone.  
An additional advantage of using multimodal data is that a model has the possibility to foresee one modality by means of another; in this way, the network can solve certain difficulties. For instance, [PERSON] et al. [82] studied a DL architecture for the objective detection of structural defects in OD images. This approach, utilizing spectral domain OCT images as a standard assessment knowledge and comparing them to highly variable assessments by ophthalmologists, can fairly evaluate the level of neuronal harm and predict glaucoma with greater accuracy.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Authors** & **Year** & **Dataset** & **Modality** & **Model** & **Application** & **AUC** & **Sensitivity** & **Specificity** \\ \hline [PERSON] & 1994 & Local dataset & Visual fields & A two-layer & Diagnosis & – & 0.65 & 0.72 \\ \hline [PERSON] et al. [84] & 1996 & Local dataset & Visual fields; OD and RNFL & A four-layer & Diagnosis: Structural & – & 0.84 & 0.86 \\  & & & measurements & & Diagnosis: Structural & – & 0.87 & 0.56 \\ \cline{3-8}  & & & & Diagnosis: Multimodal & – & 0.90 & 0.84 \\ \hline [PERSON] et al. [85] & 2015 & ORIGA; SCES* & Fundus photos & A six-layer & Diagnosis & 0.898 & – & – \\ \hline [PERSON] et al. [86] & 2016 & Local dataset & Visual fields & A four-layer & Diagnosis & 0.926 & 0.749 & 1.000 \\ \hline [PERSON] et al. [54] & 2017 & SiDRP 14-15\# & Fundus photos & VGG-19 & Diagnosis & 0.942 & 0.964 & 0.932 \\ \hline [PERSON] et al. [75] & 2018 & Local dataset; HRF; RIM-ONE & Fundus photos & ResNet50 & Diagnosis: Local dataset & 0.970 & 0.893 & 0.971 \\ \cline{3-8}  & & & & Diagnosis: HRF & 0.890 & 0.867 & 0.867 \\ \hline [PERSON] et al. [53] & 2018 & LabelMe & Fundus photos & Inception\_v3 & Diagnosis & 0.986 & 0.956 & 0.920 \\ \hline [PERSON] et al. [87] & 2018 & Local dataset & Visual fields & VGG-16 & Diagnosis & 0.966 & 0.932 & 0.826 \\ \hline [PERSON] et al. [88] & 2018 & Local dataset & Fundus photos & ResNet & Diagnosis & 0.965 & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: A review of research on glaucoma diagnosis and prognosis that use models of neural networks (chronological order).  
Several other applications of DL and networks utilizing multimodal data may be read in a well-regarded paper [74].