Computational Decision Support Systems and Diagnostic Tools

a leaf and is represented by a category or a class.36 This category can be the healthy eye class or the diseased eye class. The random forest model is based on a large number of decision trees. To classify a new patient, each tree provides a classification, and the forest chooses the classification with the most samples over all the trees in the forest.

Seventh, the ophthalmologist will use the GUI. The GUI is an automatic and interactive system that aids clinicians in making important clinical decisions.

3.4.5. Computational Decision Support System: Amblyopia

Amblyopia is an eye disease that results in poor or indistinct vision. It is caused by either no transmission or poor transmission of the visual image to the brain for a sustained period during early childhood.37,38 Detecting the disease at an early stage can increase the chances of successful treatment. Amblyopia can be corrected by patching the good eye, or by instilling topical atropine in the eye with better vision. Symptoms of amblyopia include visual disorders, poor depth perception, low sensitivity to contrast and motion, and difficulty seeing in 3D images. Amblyopia may be strabismic, anisometropic, or occlusional.

3.4.5.1.Role of computational decision support system in amblyopia

The interactive and automatic system of amblyopia helps surgeons to diagnose and treat amblyopia using a computational system such as multimedia technology, artificial intelligence (fuzzy logic), and neurophysiology in patients, such as babies, who are unable to describe their symptoms.39 Traditional treatments do not easily check gray vision, stereo vision, or confluent vision, and a 3D computational system graph can fill these diagnostic voids and treat early amblyopia in children. The computational steps for amblyopia are described below in Fig. 3.17.

After diagnosing amblyopia, an ophthalmologist determines the type of amblyopia that the patient has, and adds an optimum treatment into the database. Next, this data is distributed to other repositories through servers. The database and the analytical system in the patient data and

117

Sumeet Dua and Mohit Jain

Data Acquisition

System Database

Physiology

Fuzzy Logic

Fig. 3.17. Computational steps for diagnosing amblyopia.

expert system provide the physician with the tools to treat the amblyopia efficiently.

As mentioned above, amblyopia develops during childhood. In healthy eyes, the brain receives images from each eye and merges them into a single image. When a patient develops amblyopia, the brain cannot merge the images it receives from the eyes, and the brain’s vision for an eye stops responding. As time progresses, vision loss occurs. Once vision loss occurs, it is difficult to cure the eye with glasses or surgery, because the disease is in the brain, not in the eye. Campbell and Hess presented a pulse method that can improve eyesight using the sensitiveness of cones to red light.38,40 The parameters that will influence the effectiveness of the treatment are the frequency of light, the intensity of light, and the treatment time. Because amblyopia is physiologically connected to the brain, not limited to the eye, Campbell and Hess stimulated the amblyopia diseased eye from different directions by utilizing black-and-white bars with a different

118