Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning

A Deep Learning Model for Automatically Quantifying the Anterior Segment in Ultrasound Biomicroscopy Images of Implantable Collamer Lens Candidates

OBJECTIVE

This study aimed to develop and evaluate a deep learning-based model that could automatically measure anterior segment (AS) parameters on preoperative ultrasound biomicroscopy (UBM) images of implantable Collamer lens (ICL) surgery candidates.

METHODS

A total of 1164 panoramic UBM images were preoperatively obtained from 321 patients who received ICL surgery in the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. First, the UNet++ network was utilized to segment AS tissues automatically, such as corneal lens and iris. In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks (ALs) of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA), and sulcus-to-sulcus distance (STS). Based on the results of the latter two processes, PD, ACD, ATA, and STS can be measured. Meanwhile, an external dataset of 294 images from Huangshi Aier Eye Hospital was employed to further assess the model's performance in other center. Lastly, a subset of 100 random images from the external test set was chosen to compare the performance of the model with senior experts.

RESULTS

Whether in the internal test dataset or external test dataset, using manual labeling as the reference standard, the models achieved a mean Dice coefficient exceeding 0.880. Additionally, the intra-class correlation coefficients (ICCs) of ALs' coordinates were all greater than 0.947, and the percentage of Euclidean distance distribution of ALs within 250 μm was over 95.24%.While the ICCs for PD, ACD, ATA, and STS were greater than 0.957, furthermore, the average relative error (ARE) of PD, ACD, ATA, and STS were below 2.41%. In terms of human versus machine performance, the ICCs between the measurements performed by the model and those by senior experts were all greater than 0.931.

CONCLUSION

A deep learning-based model could measure AS parameters using UBM images of ICL candidates, and exhibited a performance similar to that of a senior ophthalmologist.

Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images

BACKGROUND

To explore the feasibility of artificial intelligence technology based on deep learning to automatically recognize the properties of vitreous opacities in ophthalmic ultrasound images.

METHODS

A total of 2000 greyscale Doppler ultrasound images containing non-pathological eye and three typical vitreous opacities confirmed as physiological vitreous opacity (VO), asteroid hyalosis (AH), and vitreous haemorrhage (VH) were selected and labelled for each lesion type. Five residual networks (ResNet) and two GoogLeNet models were trained to recognize vitreous lesions. Seventy-five percent of the images were randomly selected as the training set, and the remaining 25% were selected as the test set. The accuracy and parameters were recorded and compared among these seven different deep learning (DL) models. The precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC) values for recognizing vitreous lesions were calculated for the most accurate DL model.

RESULTS

These seven DL models had significant differences in terms of their accuracy and parameters. GoogLeNet Inception V1 achieved the highest accuracy (95.5%) and minor parameters (10315580) in vitreous lesion recognition. GoogLeNet Inception V1 achieved precision values of 0.94, 0.94, 0.96, and 0.96, recall values of 0.94, 0.93, 0.97 and 0.98, and F1 scores of 0.94, 0.93, 0.96 and 0.97 for normal, VO, AH, and VH recognition, respectively. The AUC values for these four vitreous lesion types were 0.99, 1.0, 0.99, and 0.99, respectively.

CONCLUSIONS

GoogLeNet Inception V1 has shown promising results in ophthalmic ultrasound image recognition. With increasing ultrasound image data, a wide variety of confidential information on eye diseases can be detected automatically by artificial intelligence technology based on deep learning.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy

OBJECTIVE

The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images.

METHODS

A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts.

RESULTS

The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001).

CONCLUSION

The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

Establishment and Evaluation of Intelligent Diagnostic Model for Ophthalmic Ultrasound Images Based on Deep Learning

OBJECTIVE

The goal of the work described here was to construct a deep learning-based intelligent diagnostic model for ophthalmic ultrasound images to provide auxiliary analysis for the intelligent clinical diagnosis of posterior ocular segment diseases.

METHODS

The InceptionV3-Xception fusion model was established by using two pre-trained network models-InceptionV3 and Xception-in series to achieve multilevel feature extraction and fusion, and a classifier more suitable for the multiclassification recognition task of ophthalmic ultrasound images was designed to classify 3402 ophthalmic ultrasound images. The accuracy, macro-average precision, macro-average sensitivity, macro-average F1 value, subject working feature curves and area under the curve were used as model evaluation metrics, and the credibility of the model was assessed by testing the decision basis of the model using a gradient-weighted class activation mapping method.

RESULTS

The accuracy, precision, sensitivity and area under the subject working feature curve of the InceptionV3-Xception fusion model on the test set reached 0.9673, 0.9521, 0.9528 and 0.9988, respectively. The model decision basis was consistent with the clinical diagnosis basis of the ophthalmologist, which proves that the model has good reliability.

CONCLUSION

The deep learning-based ophthalmic ultrasound image intelligent diagnosis model can accurately screen and identify five posterior ocular segment diseases, which is beneficial to the intelligent development of ophthalmic clinical diagnosis.

Construction of an end-to-end regression neural network for the determination of a quantitative index sagittal root inclination

BACKGROUND

Immediate implant placement in the esthetic area requires comprehensive assessments with nearly 30 quantitative indexes. Most artificial intelligence (AI)-driven measurements of quantitative indexes depend on segmentation or landmark detection, which require extra labeling of images and contain possible intraclass errors.

METHODS

For the initial attempt, the method was tested on sagittal root inclination measurement. This study had developed an accurate and efficient end-to-end model incorporating a convolutional neural network (CNN) based on unlabeled cone-beam computed tomography (CBCT) images for immediate implant placement diagnosis and treatment. The model took pretrained ResNeXt101 as the backbone and was constructed based on 2,920 CBCT images with corresponding angles of the tooth axis and bone axis. The performance of our CNN model was evaluated on a separate test set.

RESULTS

Our model exhibited high prediction accuracy in sagittal root inclination measurements, as evidenced by the low mean average error of 2.16°, the high correlation coefficient of 0.915 to manual measurement, and the narrow 95% confidence interval shown by Bland-Altman plots. The intraclass correlation coefficient further confirmed the measurement accuracy of our model was comparable with that of junior clinicians. The model took merely 0.001 seconds for each CBCT image, making it highly efficient. To better understand the model's quality, we visualized our end-to-end CNN model through Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, and confirmed its effectiveness in region recognition.

CONCLUSIONS

We succeeded in taking the first step in constructing the end-to-end immediate implant placement AI tool through sagittal root inclination measurements without intermediate steps and extra labeling on images.

A Deep Learning System for Automatic Assessment of Anterior Chamber Angle in Ultrasound Biomicroscopy Images

Purpose

To develop and assess a deep learning system that automatically detects angle closure and quantitatively measures angle parameters from ultrasound biomicroscopy (UBM) images using a deep learning algorithm.

Methods

A total of 3788 UBM images (2146 open angle and 1642 angle closure) from 1483 patients were collected. We developed a convolutional neural network (CNN) based on the InceptionV3 network for automatic classification of angle closure and open angle. For nonclosed images, we developed a CNN based on the EfficienttNetB3 network for the automatic localization of the scleral spur and the angle recess; then, the Unet network was used to segment the anterior chamber angle (ACA) tissue automatically. Based on the results of the latter two processes, we developed an algorithm to automatically measure the trabecular-iris angle (TIA500 and TIA750), angle-opening distance (AOD500 and AOD750), and angle recess area (ARA500 and ARA750) for quantitative evaluation of angle width.

Results

Using manual labeling as the reference standard, the ACA classification network's accuracy reached 98.18%, and the sensitivity and specificity for angle closure reached 98.74% and 97.44%, respectively. The deep learning system realized the automatic measurement of the angle parameters, and the mean of differences was generally small between automatic measurement and manual measurement. The coefficients of variation of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 measured by the deep learning system were 5.77%, 4.67%, 10.76%, 7.71%, 16.77%, and 12.70%, respectively. The within-subject standard deviations of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 were 5.77 degrees, 4.56 degrees, 155.92 µm, 147.51 µm, 0.10 mm², and 0.12 mm², respectively. The intraclass correlation coefficients of all the angle parameters were greater than 0.935.

Conclusions

The deep learning system can effectively and accurately evaluate the ACA automatically based on fully automated analysis of a UBM image.

Translational Relevance

The present work suggests that the deep learning system described here could automatically detect angle closure and quantitatively measure angle parameters from UBM images and enhancing the intelligent diagnosis and management of primary angle-closure glaucoma.

A novel retinal ganglion cell quantification tool based on deep learning

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.

Anterior Chamber Angle Assessment Techniques: A Review

Assessment of the anterior chamber angle (ACA) is an essential part of the ophthalmological examination. It is intrinsically related to the diagnosis and treatment of glaucoma and has a role in its prevention. Although slit-lamp gonioscopy is considered the gold-standard technique for ACA evaluation, its poor reproducibility and the long learning curve are well-known shortcomings. Several new imaging techniques for angle evaluation have been developed in the recent years. However, whether these instruments may replace or not gonioscopy in everyday clinical practice remains unclear. This review summarizes the last findings in ACA evaluation, focusing on new instruments and their application to the clinical practice. Special attention will be given to the comparison between these new techniques and traditional slit-lamp gonioscopy. Whereas ultrasound biomicroscopy and anterior segment optical coherence tomography provide quantitative measurements of the anterior segment’s structures, new gonio-photographic systems allow for a qualitative assessment of angle findings, similarly to gonioscopy. Recently developed deep learning algorithms provide an automated classification of angle images, aiding physicians in taking faster and more efficient decisions. Despite new imaging techniques made analysis of the ACA more objective and practical, the ideal method for ACA evaluation has still to be determined.