# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI-Based Systems in Glaucoma
#### 4.3.3 Progress and Prognosis of Glaucoma  
After glaucoma is detected, it follows the prediction of the course of the disease to avoid over- or under-treatment. However, glaucoma increases in a _non-linear_ manner and can be influenced by certain parameters, presenting significant prognostic provocation. There are no extensively acknowledged clinical tests that can predict the development of glaucoma, and evaluation is based largely on the skills and expertise of clinicians, often requiring many visits to hospitals.  
_Trajectory based prediction_  
Glaucoma prognostic models require serial patient data to develop progression trajectories. For example, Kalman filtering, a widely used prognostic technique, plays a role in increasing efficiency (with 29% fewer tests) and decreasing delays (57% earlier than fixed-interval monitoring) in detecting disease development and foreseeing mean VF and IOP computations for normal-tension glaucoma.  
By means of archetypal analysis, [PERSON] et al. evolved a calculation technique regarding the adaptation rate of weights related to different architectures and developed a foreseen representation based on a large dataset with serial tests. This representation of glaucoma reached a precision of 0.77 in predicting glaucoma progression.  
[PERSON] et al. [80] yielded similar prediction models by means of trajectories.  
_Prediction at a single point in time_  
Several DL models have predicted glaucoma progression by means of data at a single moment in time, which, if feasible, are more efficient than NNs that use serial data. However, it is very difficult to deploy such architectures, because the forecasts have to be made preferably a long time before the clinical manifestations of the disease.  
Two such models are shown in Table 4. [PERSON] et al. [94] developed a DL architecture to predict OD or VF anomalies using fundus images. Such a structure reached an AUC of 0.77 for evaluating glaucoma progress 4-7 years before clinical symptoms, and the AUC was greater (0.88) when prognosing progress 1-3 years before disease onset.  
\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] & 2018 & ADAGES5; & Fundus photos & ResNet50 & Diagnosis & 0.910 & 0.840 & 0.830 \\  & & DIGS\(\sim\) & & & & & & \\ \hline [PERSON] & 2019 & Local dataset & OCT scans; & ResNet34 & Diagnosis & 0.944 & 0.900 & 0.800 \\  & & & fundus photos & ResNet & Diagnosis & 0.996 & 0.962 & 0.977 \\ \hline [PERSON] et al. [91] & 2019 & Local dataset & OCT scans & A 12-layer CNN & Diagnosis & 0.937 & 0.825 & 0.939 \\ \hline [PERSON] et al. [92] & 2019 & Local dataset & AS-OCT scans & VGG-16 & Diagnosis & 0.96 & 0.90 & 0.92 \\ \hline Normando & & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & & \\ \hline [PERSON] et al. [94] & 2020 & OHTS+ & Fundus photos & MobileNet\_v2 &
\begin{tabular}{c} Prognosis: \\ 1-3 years \\ \end{tabular} & 0.88 & – & – \\ \cline{4-7}  & & & & & & & \\ \cline{4-7}  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Another study [93] proposed a CNN-assisted method to predict glaucoma progression using an assay of apoptotic retinal cells (DARC). The CNN structure in the above research was utilized not to foresee progress but to identify DARC-positive stained cells in retinal fluorescence images. By means of RNFL (retinal nerve fiber layer) OCT measurements at 18 months as a reference, the CNN-assisted DARC experiment showed a sensitivity of 0.857 and a specificity of 0.917 with an AUC of 0.89 in changing rapidly progressing eyes from steady ones.