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Random Forest Algorithm-Based Ultrasonic Image in the Diagnosis of Patients with Dry Eye Syndrome and Its Relationship with Tear Osmotic Pressure

The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) μm and (29.1 ± 30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (−6.31 ± 2.82) μm, and that in group B was (−6.45 ± 3.06) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was (−3.78 ± 1.13) μm in group A and (−7.09 ± 2.05) μm in group B (P < 0.05). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome.


Introduction
Dry eye syndrome is an eye disease with high incidence, also known as dry corneal conjunctivitis [1]. It is defined as tear abnormality and eye abnormality caused by various factors. Dry eye syndrome causes eye discomfort, visual impairment, and potential threats to eye health. There are also changes in tear film osmotic pressure with dry eyes and the involvement of eye inflammation [2]. This definition is mainly based on the causes, pathogenesis, and severity of dry eye. Patients with dry eye often cause subjective discomfort feelings which are as follows: burning sensation, increased secretions, dryness, redness, foreign body sensation, eye pain, eye itching, tears, photophobia, eye fatigue, and vision loss, etc. [3]. With the rapid development of modern society and the advent of the information age, the amount of information has surged. Under the interference of various factors such as the popularization of computers and mobile phones, the prevalence of dry eye syndrome in ophthalmic diseases has increased rapidly. Most patients are treated for serious eye discomfort, such as eye fatigue, irritation, pain, dryness, and foreign body sensation, which has serious impact on people's daily work and quality of life [4]. Therefore, timely diagnosis and prevention of dry eye play an important role.
At first, the medical staff only diagnosed the patients' eyes according to eye knowledge and experience. Therefore, the reliability of diagnosis results is not very high, and the accuracy of diagnosis results is poor. The emergence of the ophthalmic ultrasonic diagnostic system has brought convenience to medical personnel and patients. Relying on the ophthalmic ultrasound diagnosis system, medical personnel can diagnose ocular diseases simply, quickly, and effectively [5]. In recent years, with the rapid development of various ophthalmic imaging technologies, more comprehensive ophthalmic information has been provided for ophthalmologists. The ability of medical personnel to assess eye diseases was significantly improved. Ultrasound imaging has become the main measure to detect eye diseases, especially retinal diseases [6]. Doctors need to assess patients' eye diseases based on these ultrasound images. These images need to be processed by machine first. Some data and information will be obtained after image processing. These data and information are the basis for doctors to diagnose diseases [7]. Nowadays, the commonly used ophthalmic ultrasound image processing in hospitals mainly has the problems of poor accuracy of detection results, insufficient function of equipment system, and too mechanized operation. Therefore, the use of artificial intelligence algorithms for medical image processing can significantly improve the efficiency of disease diagnosis. The use of artificial intelligence algorithms for medical image processing can also remove the influence of subjective and environmental factors on the diagnosis results [8]. Currently, there are many studies on the application of artificial intelligence in eye diseases. However, the application of the random forest algorithm in ultrasound diagnosis of dry eye is quite lacking.
Therefore, in this study, we applied ultrasound based on the random forest algorithm to detect patients with dry eye syndrome and explore its application value in the diagnosis of dry eye syndrome patients. Additionally, we analyzed the relevance between the degree of dry eye with the tear osmotic pressure, expected to provide more effective clinical diagnostic indicators for patients with dry eye.

Research Objects.
From June 2018 to June 2021, 100 patients with dry eye syndrome were collected in the hospital. A total of 100 effective cases were collected, including 42 male patients, with an average age of 48:31 ± 4:47 years. There were 58 female patients, with an average age of 51:28 ± 5:67. The patients were randomly divided into group A (conventional ultrasound) and group B (ultrasound based on the random forest algorithm), with 50 patients in each group. The measurement results of A-ultrasound (onedimensional ultrasonic amplitude wave type-biological measurement and qualitative tissue) were used as the gold standard to evaluate the results of the two groups. In addition, the degree of dry eye was determined by OSDI questionnaire [9] and DR-1 TFLL test [10]. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The subjects included in the study have signed informed consent, and this study was approved by ethics committee of hospital.
Exclusion criteria were as follows: (1) patients who cannot cooperate with ultrasound examination, (2) patients wearing invisible time less than one week, (3) previous eye surgery or suffered severe trauma, (4) patients with keratitis or corneal scar, and (5) active lesions in the eye.

Detection
Method. All patients were examined in visual inspection center. Comprehensive eye examination includes slit lamp microscopy, fundus examination, uncorrected visual acuity, best corrected visual acuity, and noncontact tonometer. Then, DR-1 and OSDI questionnaires were used to evaluate the degree of dry eye. Central corneal thickness (CCT) was measured by A-ultrasound and ultrasound based on the random forest algorithm, respectively.

CCT Measurement.
CCT measurement was carried out by a diagnostic worker with rich experience in using type Aultrasound. A drop of surface anesthetic (oxybuprocaine hydrochloride eye drops) was injected into the conjunctival sac of the subject. Measurement starts after 1 min. The subject took a flat posture, indicating that the subject looked at the viewpoint on the ceiling above the head. Gently contact the corneal center vertically with a sterilized A-scan ultrasound probe. Read the thickness value of the corneal center. Measured 10 times and finally take the minimum value into statistics. According to the manufacturer's advice, repeated measurement three times and calculate the average. According to the test method recommended by the manufacturer, all measurement instruments are calibrated before each measurement. All ultrasonic measurements were performed by the same trained diagnostic staff. The ultrasonic inspection method based on random forest algorithm is the same.

OSDI
Questionnaire. Doctors used DR-1 and guide patients to complete the questionnaire. There was a total of 12 main questions in the questionnaire: foreign body sensation, eye discomfort, blurred vision, decreased vision, and fear of light. In addition, there were also whether the eyes will have discomfort symptoms when using computer phones, watching TV, dry air, sandstorm weather, reading newspaper, air conditioning rooms, and night driving. Each question was divided into five grades according to the frequency or severity of eye discomfort. Each level gives the corresponding score: 1 level (no discomfort) corresponding to 0 points. Level 2 (occasional occurrence) corresponds to 1 point. Level 3 (half probability) corresponds to 2 points. Level 4 (frequent occurrence) corresponds to 3 points. Level 5 (always happens) corresponds to 4 points. OSDI scoring equation was as follows: sum of all scores × 25/total number of questions = final score.

Tear Osmotic Pressure Measurement.
Before inspection, the use of standard calibration solution calibration instrument adjusts the 10 μL liquid shifter. The measurement range was adjusted to 3 μL. At least 3 μL tears were taken from the inferior fornix along the conjunctival fornix 2 Computational and Mathematical Methods in Medicine level, and the eyes of the patients were turned to the other side of the gun head of the liquid transfer device when the tears were taken. Then, it was placed in 200 μL EP tube. Centrifuge with a microcentrifuge for about 1 second and replace the gun after suction 3 μL of upper tears. Pull out the metal rod in the osmometer detection tank and put the filter paper with diameter of 0.3 mm into the detection tank.
The filter paper was soaked with 3 μL upper tears to detect the reduction of the metal rod of the groove, and the gear position was adjusted to the detection position for detection. After 80 s detection, record the data displayed on the screen. The interval between the two measurements should not be less than 1 minute [11].
2.3. Random Forest Algorithm. The random forest algorithm is an important method in machine learning. It builds Bagging integration through decision tree and further introduces random attribute selection in decision tree training [12]. In this study, all the features of the dataset and seven labels (real classification, six prospects, and one background) data are input and trained in multiple decision trees. Each node in the decision tree is a weak binary classifier, and each node will select the parameters that will maximize the classification index. This study uses information gain ratio (IGR) as a classification index. The final leaf node example includes a discriminator for seven categories. The relevant calculation equations in the decision tree construction process are shown in equations (1)-(4). Gain IGRðM, nÞ is the information gain rate. M represents the original sample set. n represents the attribute, that is the extracted features. E i represents the proportion of class i samples. A represents the total number of categories. a represents a certain category. EntðMÞ represents the information entropy of the sample set and the purity of M. The smaller the value is, the higher the purity of M is. GainðM, nÞ is the information gain (information gain), representing the purity boost obtained when classifying using feature n. The larger the value, the higher the purity.
IVðnÞ denotes the intrinsic value of the characteristic n. The more possible values of n, the larger the IVðnÞ value. The higher the value of IGRðM, nÞ is, the better the effect is when feature n divides samples. n is regarded as a strong feature.
The random forest model needs to build multiple decision trees. The training process of a single decision tree is shown in Figure 1. Sample a represents a single pixel. When a is entered, a will arrive at the leaf node through the root node of a decision tree through several intermediate nodes.
Each intermediate node represents a weak classifier, which stores a certain feature and the corresponding threshold. In the middle node sample, a will be classified into two categories, respectively, into the left node and right node. Repeat this step until reaching the final leaf node. Leaf nodes store the distribution probability of each category, which can help analyze the category of sample a. Finally, combined with the processing results of multiple decision trees, the final analysis results of a can be obtained. In the past, random forests often use average voting to analyze the processing results of multiple decision trees. The category with the largest probability is the final category judgment result of a.
The classification score of this decision tree on a is score ðaÞ = ðIGR Root Node + IGR2 + IGR6Þ/3.
The average vote has some drawbacks. The average vote only extracts information from leaf nodes. Only when the number of decision trees is large can more accurate classification results be obtained. If the number of decision trees is large, the time consumed by training and classification will be greatly extended [13]. In order to solve this problem, we adopted the improved random forest model. The main improvement is to replace the average voting method with the weighted voting method in the voting process. The information of intermediate nodes is also taken into account when voting. The classification effect of the decision tree on the samples is judged by the average information gain rate obtained by all the nodes of the tree.
The larger the value of the decision tree, the better the classification effect of the sample. The method of weighted voting is shown in equations (5)- (7).
Score a, P o represents the normalized weight of decision tree o. Scoreða, tree o Þ represents the score of decision tree o. O represents the number of decision trees. J represents the depth of each decision tree. In addition, the classification of each pixel is a relatively independent process. Therefore, the classification time can be shortened by parallel computing.
In the process of random forest classification, a classification feature is randomly selected from F features as the intermediate node in the decision tree. According to the traditional method, we set F to log 2 N (N is the total number of features 15); that is, F is 4. The number of trees has an important impact on the time and accuracy of classification. According to our previous work, we find that when the number O of trees and the depth J o of trees reach a certain range, the accuracy of classification is close to saturation. This shows that the increase of numerical value can only provide a small improvement in the accuracy of classification, but it will greatly increase the time consumption. Besides, the influence of the number and depth of decision trees on CME classification results was also analyzed.

Statistical
Methods. SPSS22.0 software was used for statistical analysis of the data, and P < 0:05 indicated statistical significance. Kolmogorov-Smirnov test was used to evaluate that the distribution of data set belonged to normal distribution. The difference between the results of the measurement method was compared by paired sample t-test. Correlation analysis uses Spearman correlation analysis. The Bland-Altman analysis method was used for consistency analysis between test results. Figure 2 shows the effect of the number and depth of trees on the CME classification results in this study. The results show that when the number of trees is 16 and the depth of trees is 12, the values of true positive volume coefficients (TPVF) and false positive volume coefficients (FPVF) are close to the relative saturation state with little change. So, we use O = 16 and J = 12 as the parameters of the random forest.

Simulation
Results of Algorithm. The input data were 3D images centered on macular, which were collected from 19 patients with retinal macular disease. After random forest classification, the final images were divided into seven categories: MH, CME, and four retinal structures (L1: NFL; L2: GCL, IPL, INL, and OPL; L3: ONL, ISL; L4: CL, OSL, VM, and RPE) and background region. The results were shown in Figure 3.
We used the Deiss similarity coefficient (DCS), true positive volume coefficient (TPVF), and false positive volume coefficient (FPVF) as the measurement results [14]. The DCS of CME and MH was 82.65% and 79.34%. TPVF was 79.86% and 74.17%. FPVF was 0.11% and 0.03%. The DCS of retinal four-layer structure was 85.41%, 93.21%, 92.60%, and 93.71%, respectively. The TPVF was 87.61%, 94.02%, 91.28%, and 93.61%, respectively. The FPVF was 0.72%, 1.40%, 0.60%, and 0.44%. The data results showed that the proposed random forest algorithm was superior to the traditional image processing method in the segmentation of retinal layer and diseased region, as shown in Figures 4-6. 3.3. General Information of Subjects. According to the subjective description of eye discomfort in 100 dry eye syndrome patients, dryness was the most common symptom, accounting for 48%. The second was foreign feeling, accounting for 27%. Eye itching accounted for 8%. Burning sensation accounted for 4%. Patients who did not complain of eye discomfort accounted for 13%, as shown in Figure 7.

Consistency Analysis of CCT Average Values Measured in Each
Group. The average CCT values of group A and group B were (27:8 ± 30:6) μm and (29:1 ± 30:9) μm, respectively. The consistency analysis of the two groups showed that the 95% confidence interval of group A and group B was 22.7-34.2 μm. There was no significant difference in CCT values measured by the two methods (P > 0:05), and there was a high correlation between the two groups, as shown in Table 1.

Consistency Analysis of Central Corneal Thickness Data in Moderate and Severe Dry Eye Patients.
There was no significant difference in the mean CCT between group A and group B in patients with moderate dry eye syndrome (P > 0:05), while the 95% confidence interval was −7.73− 5.20. In patients with severe dry eye syndrome, the CCT  Computational and Mathematical Methods in Medicine mean values measured in group A and group B were significantly different, and there was a high correlation between the two groups (95% confidence interval was −7.05~−5.11, P < 0:05), as shown in Figure 8.

Correlation between Tear Osmotic Pressure and Dry Eye
Severity. The results showed that the average tear osmotic pressure was 316.5 mOsm/kg for moderate dry eyes and 403.6 mOsm/kg for severe dry eyes, as shown in Figure 9.

Computational and Mathematical Methods in Medicine
Spearman bilateral test was performed on tear osmotic pressure and dry eye severity in patients with dry eye. The results showed that tear osmotic pressure was significantly correlated with dry eye severity (r = 0:779; P < 0:05).

Discussion
With the development of science and technology, dry eye syndrome patients are more and more. There are many reasons for dry eye syndrome. Dry eye syndrome is a general term for a variety of diseases caused by abnormal tear quality or quantity or abnormal dynamics caused by any reason, resulting in decreased tear film stability and accompanied by eye discomfort (or) ocular surface tissue lesions [15]. Pathogenic causes include eye local inflammation, systemic diseases, environmental impact, excimer laser refractive surgery, and drug effects [16]. Dry eye syndrome tear film rupture time will be shortened. In addition, there are excessive lipid accumulations caused by dry eye syndrome. Dry eye syndrome tester DR-1 can be based on optical interference photography that can visually display the central corneal tear film surface lipid layer morphology, in accordance with different levels of assessment of excessive lipid accumulation caused by the degree of dry eye strength. Accurate judgment of dry eye syndrome condition is more conducive to timely detection and treatment.
This study was mainly to compare the results of ultrasound and gold standard A-ultrasound based on the random forest algorithm. According to the research results, the   Computational and Mathematical Methods in Medicine ultrasonic imaging effect based on the random forest algorithm was very close to the gold standard. The effect was good, and according to the results of each index, it was obviously better than the graph cut method in traditional ultrasound. Corneal thickness obtained by B-ultrasound was the gold standard for corneal thickness measurement. However, the differences in operating angles, the fear of patients on the machine, and the replacement of diagnostic personnel at any time may all affect the accuracy of the measured values [17].
In addition, some studies had shown that the use of surface anesthetics will affect corneal edema caused by corneal epithelium and thus affect the tear film, making the final measurement value of CCT slightly thicker than the actual results [18]. In this study, we applied ultrasound based on the random forest algorithm to compare with the detection results of A-ultrasound. The results showed that there was no significant difference in CCT values measured by the two methods, and there was a high correlation between the two groups. This showed that the detection effect of ultrasound based on the random forest algorithm was consistent with that of gold standard and can be accepted in clinical practice. It can be used as an alternative to the traditional ultrasonic detection method. The difference of central cor-neal thickness between the two test results was small, which can be used for the diagnosis of dry eye disease. The study of tear osmotic pressure and dry eye severity showed that with the aggravation of dry eye, tear osmotic pressure increased significantly. This was consistent with the research results of Liu et al. (2020) [19]. Additionally, van Setten (2019) [20] proposed that different osmotic pressures may be the key mechanism leading to cell death, inflammation, apoptosis, and goblet cell loss observed in dry eye. Willshire et al. [21] mentioned that the osmotic pressure of tear is usually used for the diagnosis of dry eye. However, the study on dry eye parameters is relatively simple. In the future research of ultrasonic diagnosis of dry eye disease based on the random forest algorithm, the difference of curvature, the difference of anterior and posterior corneal surface, spherical aberration, the trend of corneal thickness change, and the difference of corneal thickness measurement at different measuring points can also be compared. Further studies should focus on keratoconus detection differences and other parameters [22,23]. It provides more accurate and more efficient data theoretical support for clinical diagnosis, which is helpful for the improvement and development of clinical dry eye diagnosis and treatment.   Figure 9: Relationship between tear osmotic pressure and dry eye severity. Note: * indicates statistically significant differences (P < 0:05).

Conclusion
The study is aimed at exploring the correlation between tear osmotic pressure with dry eye through ultrasonic images. The results showed that tear osmotic pressure could become an indicator of dry eye syndrome diagnosis. Combined clinical symptoms can be used as an indicator for rapid diagnosis of dry eye syndrome severity. The results of the two detection methods in different degrees of dry eye syndrome showed little difference and had significant correlation. It was very close to the ultrasonic method and had small variability. The results of this study can provide reference for the treatment of dry eye syndrome. However, the analysis of the accuracy of ultrasonic measurement in this study is not comprehensive enough, and further analysis is needed. This study shows the auxiliary role of artificial intelligence algorithm in clinical practice and suggests a good development prospect of artificial intelligence.

Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
The authors declare no conflicts of interest.