# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### Artificial Intelligence and Cataract
#### 4.1.4 Calculation of the Power of Intraocular Lenses  
With AI, one can research complex non-linear relations between ocular parameters and calculate IOL power, customized to each individual's eye characteristics. It is also appropriate to use the effects of postoperative results, such as postoperative refractive status, to calibrate surgeon performance parameters, such as SIA (surgeon induced astigmatism). Two formulas that use AI are the Hill Radial Basis Function (Hill-RBF) and the Kane formula. The Hill-RBF technique was developed using 12,000 eyes with measurements taken with the Haag-Streit Lenstar optical biometer [69]. The Kane method was developed by means of high-quality cloud computing, which employed regression and ML models to refine IOL power predictions [70]. Accuracy results (comparison of postoperative refractive errors estimated by formulas and actual ones) are shown in Table 3. It is noted that some improvements in eye care are possible by using short axial length. Many studies on eyes with a wide diversity of severe refractive conditions are imperatively necessary to confirm the above formulas for hospital practice. The Hill-RBF method was also compared with the traditional Barrett Universal II method and the SRK/T Formula. The formula based on AI was better than both the Barrett and SRK/T formulas [71]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Author** & **Data Source** & **Method** &
\begin{tabular}{c} **Definition of Gold** \\ **Standard/Ground** \\ **Truth** \\ \end{tabular} & **Performance** \\ \hline
2017 & [PERSON] et al. [67] & \begin{tabular}{c} 5495 training, \\ 2355 testing \\ \end{tabular} & \begin{tabular}{c} Caffe: Feature \\ extraction SoftMax: \\ Detection \\ and grading \\ \end{tabular} & \
\begin{tabular}{c} Labelled fundus \\ images by \\ ophthalmologists \\ \end{tabular} &
\begin{tabular}{c} Accuracy a = 94.07\% b \\ Accuracy a = 90.82\% c \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. & \begin{tabular}{c} Beijing Tongren Eye \\ Centre’s clinical \\ database, \\ 1606 testing \\ \end{tabular} & \begin{tabular}{c} DCNN: \\ Detection and grading \\ \end{tabular} & \
\begin{tabular}{c} Labelled fundus \\ images by graders \\ \end{tabular} &
\begin{tabular}{c} AUC = 0.935 b \\ AUC = 0.867 c \\ \end{tabular} \\ \hline
2018 & [PERSON] et al. & Not described & \begin{tabular}{c} DCNN: \\ Feature extraction \\ Random forest: \\ Detection and grading \\ \end{tabular} & \
\begin{tabular}{c} Labelled fundus \\ images by \\ ophthalmologists, \\ crosschecked by \\ graders \\ \end{tabular} &
\begin{tabular}{c} AUC = 0.970 b \\ Sensitivity = 97.26\% b \\ Specificity = 96.92\% b \\ \end{tabular} \\ \hline
2018 & [PERSON] et al. [53] & \begin{tabular}{c} Beijing Tongren Eye \\ Centre’s clinical \\ database, 7030 training, \\ 1000 testing \\ \end{tabular} & \begin{tabular}{c} ResNet 18: Detection \\ ResNet 50: Grading \\ \end{tabular} & \
\begin{tabular}{c} Labelled fundus \\ images by graders \\ \end{tabular} &
\begin{tabular}{c} AUC = 0.972 b \\ AUC = 0.877 c \\ \end{tabular} \\ \hline
2019 & [PERSON] and [PERSON] [68] & Multiple online \\ databases, 400 training, 400 testing & \begin{tabular}{c} Pre-trained CNN: \\ Feature extraction \\ SVM: \\ Detection and grading \\ \end{tabular} & \
\begin{tabular}{c} Labelled fundus \\ images by \\ ophthalmologists \\ \end{tabular} &
\begin{tabular}{c} Accuracy a = 100\% b \\ Accuracy a = 92.91\% c \\ \end{tabular} \\ \hline  & \multicolumn{3}{p{56.9 pt}}{Legend: DCNN: Deep Convolutional Neural Network; CNN: Convolutional Neural Network; SVM: Support Vector Machine; ResNet: Residual Neural Network. (a) Indicates the proportion of correctly classified images from the total images tested. (class b) non-cataract versus cataract; (class c) no cataract, mild, moderate, and severe [12].} \\ \end{tabular}
\end{table} 
Table 2: Studies on automatic fundus-based cataract detection and grading.