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LSL is used after glaucoma surgery to decrease the resistance to outflow under the scleral flap. Yu et al. determined the effect of ASOCT in decision making by comparing two observer decisions and seven eyes, each from a different patient. Yu et al. determined that all the patients had poorly controlled IOP. ASOCT produces images of the sclera flap, the sclera, the internal ostium, the bleb cavity, and the bleb wall. Using clinical examination alone, LSL was recommended to all the patients due to the presence of a deep AC and a poorly formed bleb, whereas, using ASOCT imaging, LSL was recommended to only five of the seven patients. The two cases that did not required LSL indicated good aqueous flow through the bleb. ASOCT showed the position of sclera flap relative to the sclera, the presence or absence of a patent hyporeflective sub flap space, and bleb wall thickening, none of which were seen with the clinical examination at the slit lamp. As shown in this case study, images obtained using ASOCT impact clinician decisions of whether or not to undertake LSL. Therefore, ASOCT imaging can save the resources, money, and time of patients and ophthalmologist.

3.4.4.2. Computational decision support system: RD

RD is an eye disease that occurs when the sensory and pigment layers of the retina separate. If untreated, RD can lead to vision loss or blindness, and is considered an ocular emergency that requires immediate medical attention. RD generally occurs in middle age and elderly people.

The types of RD include rhegmatogenous RD, exudative RD, and tractional RD. The symptoms of RD include heaviness in the eye, an increased number of floaters, a sudden decrease in vision, and central visual loss. Treatment can take many forms, including cryopexy and laser photocoagulation, scleral buckle surgery, and pneumatic retinopexy. RD can be prevented by avoiding direct trauma to the eye, cataract surgery, and a sudden increase or decrease in eye pressure.

3.4.4.3. Role of computational system

A computational system can predict the development of proliferative vitreoretinopathy (PVR) in patients who are experiencing rhegmatogenous RD. PVR is the predominant cause of failure of RD surgery.35 Therefore, by identifying the high risk for PVR, a computational system will help

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Fig. 3.16. Computational steps for diagnosing RD.

surgeons select specific treatments. Even with advancements in RD surgery, the risk of RD is high in patients with established PVR. A computational system will aid clinicians in identifying PVR and avoiding risks involved in surgery. The computational procedure for diagnosing PVR is described in Fig. 3.16.

First, data are obtained from patient gene expressions or genomic data from those patients who have undergone rhegmatogenous and have a higher PVR and from those patients who have healthy eyes.

Second, missing values in the gene dataset are determined using the KNN impute function. KNN replaces the missing values from the nearest neighbor column, which is computed using Euclidean distance. If the nearest neighbor column also has missing values, then the next neighbor will be used. Once all missing values in a gene are filled, then the feature extraction is performed.

Third, a training set will be developed. The complete gene dataset will be used to train the model for classifiers or to construct the model, so that

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it can be used to test the data. The training set can also be used to classify the healthy eyes and the diseased eyes.

Fourth, feature extraction will be performed to balance the large number of features present in the genome dataset with the small number of samples. Feature extraction can be performed by SNP measurement, so that SNP helps in dimensionality reduction. Examples of features extracted are CTGF, PDGF, PDGFR_, PI3KCG, EGF, FGF2, MIF, MMP2, and MMP7. The main objective of SNP is to eliminate irrelevant features, which highlights important information about the disease and reduces the cost of storing a large dataset. SNP also helps to extract hidden relationships and correlations.

Fifth, feature selection will be performed. The final aim of SNP is to have a good disease classifier and to minimize the prediction error of the model. The information gain method is used to reduce the uncertainty degree in the data. By selecting important features, the clinician speeds up the learning process or removes the effect of the curse of dimensionality. Feature selection can be performed by ranking features so that the clinician can determine the rank or cost of the features or the attributes. Based on the rank, the clinician can select the features. Methods by which the clinician can rank his or her features are ChiSquared attribute, GainRatio attribute, and infogain attribute.

Sixth, K fold cross-validation using classifiers will be performed. Classification can be performed using 10-fold cross-validation. In 10-fold crossvalidation, the dataset or training data are partitioned into 10 sub datasets. From the 10 datasets, a single dataset is selected as the testing the model; the remaining nine datasets are used for training. The cross-validation process is then repeated once with each of the 10 datasets. The 10 results from the folds can then be averaged to produce a single result. The classifiers that can be used for cross-validation are SVM, Naïve Bayes, Random Forest, and Decision Tree. Naïve Bayes uses values of each SNP in patients known to have PVR. This information is used to predict the class of a new patient and assumes that each SNP is independent from every other SNP. SVM constructs hyperplanes in the n-dimensional space of the input data and optimizes the separation between the diseased eye data and the healthy eye data. A decision tree starts as a single node, representing the training samples. If all the samples are from the same class, then the node becomes

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