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以人工智慧技術診斷早產兒視網膜病變和糖尿病視網膜病變

為了解決VW usa的問題，作者Spandana Vadloori 這樣論述：

Retinopathy is one of the most frightening reasons resulting in blindness. It occurs in diabetic patients suffering from diabetes for the long term, called diabetic retinopathy (DR), and it also occurs in those premature infants born with very low birth weight, called retinopathy of prematurity (RO

P). While the occurrence and progression of the DR include the presence of exudates, hemorrhages, microaneurysms, etc., in the patients, ROP can be characterized by abnormal retinal blood vessel growth. Timely detection of these diseases followed by timely treatment can prevent the patients from bli

ndness and also considerably improve the visual acuity of high-risk patients. Computer-aided diagnosis is a computer-based system for diagnosing the disease to assist ophthalmologists in the diagnosis of disease. Evaluating the medical data manually is laborious and demands expertise in the field fo

r diagnosing these diseases. Hence in the present study, we used machine learning and transfer learning approaches to classify the DR datasets and ROP retinal images datasets.For the classification of ROP, we used retinal fundus images and applied pre-trained deep learning models such as VGG16, VGG1

9, Inception V3, MobileNet, and DenseNet for this classification of the disease. We initially performed classification of ROP and No ROP for the presence of disease or no disease. Then, we classified the disease severity as mild-ROP and severe-ROP. Our results showed that the pre-trained model, VGG1

9 was the best among other models to determine whether preterm infants have ROP. It showed 96% accuracy, 96.6% sensitivity, and 95.2% specificity. In the classification of the ROP disease severity, the VGG19 model showed an accuracy of 98.82%, 100% of sensitivity, and 98.41% of specificity. To evalu

ate the reliability of our best model performance, we further carried out a 5-fold cross-validation where the VGG19 model exhibited high accuracy in predicting ROP. These findings can aid in developing computer-aided diagnoses.Anomalies and variations in retinal blood vessels such as vessel angle an

d vessel width of arteries and veins may be associated with the occurrence and result in the ROP progression. We tested if this hypothesis was associated with the severity of ROP. Computationally, we determined the vein–vein, and artery–artery angles in the temporal quadrants, the temporal vein angl

e (VA) and temporal artery angle (AA), in No ROP and different stages of ROP. We determined the retinal vessel width, temporal vein width (VW) and temporal artery width (AW), by employing the Radon transform method. Our results showed a significant decrease in AA and VA and an increase in AW and VW

with the increase in ROP severity (all P < 0.0001). Furthermore, we observed a positive correlation (both P < 0.0001) between AA vs VA and AW vs VW. The AA negatively correlated with the AW (r = −0.162, P = 0.0314). Vessel tortuosity was related to the development of retinal disease. Here, we also d

etermined the artery tortuosity and vein tortuosity at the temporal side of the retina from No ROP to stage 3 ROP. We noticed a gradual increment in the tortuosity as increasing severity of the disease in both artery and vein (both P < 0.0001). We also examined the correlation between tortuosity, an

gles and widths, and found a significant negative correlation between AT vs AA (r = -0.485, P