DICOM PS3.17 2017a - Explanatory Information

DDD.2 Use Cases
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## DDD.2 Use Cases

Figure DDD.2-1. Schematic Representation of the Human Eye

Figure DDD.2-2. Sample Report from an Automated Visual Field Machine

### DDD.2.1 Evaluation For Glaucoma

The diagnosis and management of Glaucoma, a disease of the optic nerve, is the primary use of visual field testing. In this regard, automated visual fields are used to assess quantitatively the function of the optic nerve with the intent of detecting defects caused by glaucoma.

The first step in analyzing a visual field report is to confirm that it came from the correct patient. Demographic information including the patient's name, gender, date of birth, and perhaps medical record number are therefore essential data to collect. The patient's age is also important in the analysis of the visual field (see below) as optic nerve function changes with age. Finally, it is important to document the patient's refractive error as this needs to be corrected properly for the test to be valid.

Second, the clinician needs to assess the reliability of the test. This can be determined in a number of ways. One of these is by monitoring patient fixation during the test. To be meaningful, a visual field test assumes that the subject was looking at a fixed point throughout the test and was responding to stimuli in the periphery. Currently available techniques for monitoring this fixation include blind spot mapping, pupil tracking, and observation by the technician conducting the test. Blind spot mapping starts by identifying the small region of the visual field corresponding to the optic nerve head. Since the patient cannot detect stimuli in this area, any positive response to a stimulus placed there later in the test indicates that the patient has lost fixation and the blind spot has "moved". Both pupil tracking and direct observation by the technician are now easily carried out using a camera focused on the patient's eye.

Figure DDD.2-3. Information Related to Test Reliability

Another means of assessing the reliability of the test is to count both false positive and false negative responses. False positives occur when the subject presses the button either in response to no stimulus or in response to a stimulus with intensity significantly below one they had not detected previously. False negatives are recorded when the patient fails to respond to stimulus significantly more intense than one they had previously seen. Taken together, fixation losses, false positives, and false negatives provide an indication of the quality of the test.

The next phase of visual field interpretation is to assess for the presence of disease. The first aspect of the visual field data used here are the raw sensitivity values. These are usually expressed as a function of the amount of attenuation that could be applied to the maximum possible stimulus such that the patient could still see it when displayed. Since a value is available at each point tested in the visual field, these values can be represented either as raw values or as a graphical map.

Figure DDD.2-4. Sample Output from an Automated VF Machine Including Raw Sensitivity Values (Left, Larger Numbers are Better) and an Interpolated Gray-Scale Image

Because the raw intensity values can be affected by a number of factors including age and other non-optic nerve problems including refractive error or any opacity along the visual axis (cornea, lens, vitreous), it is helpful to also evaluate some corrected values. One set of corrected intensity values is usually some indication of the difference of each tested point from its expected value based on patient age. Another set of corrected intensity values, referred to as "Pattern deviation or "Corrected comparison" are normalized for age and also have a value subtracted from the deviation at each test point, which is estimated to be due to diffuse visual field loss This latter set is useful for focal rather than diffuse defects in visual function. In the case of glaucoma and most other optic nerve disease, clinicians are more interested in focal defects so this second set of normalized data is useful.

Figure DDD.2-5. Examples of Age Corrected Deviation from Normative Values (upper left) and Mean Defect Corrected Deviation from Normative Data (upper right)

For all normalized visual field sensitivity data, it is useful to know how a particular value compares to a group of normal patients. Vendors of automated visual field machines therefore go to great lengths to collect data on such "normal" subjects to allow subsequent analysis. Furthermore, the various sets of values mentioned above can be summarized further using calculations like a mean and standard deviation. These values give some idea about the average amount of field loss (mean) and the focality of that loss (standard deviation).

A final step in the clinical assessment of a visual field test is to review any disease-specific tests that are performed on the data. One such test is the Glaucoma Hemifield Test, which has been designed to identify field loss consistent with glaucoma. These tests are frequently vendor-specific.

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DICOM PS3.17 2017a - Explanatory Information