Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Rao, Divya Parthasarathy; Savoy, Florian M.; Tan, Joshua Zhi En; Fung, Brian Pei-En; Bopitiya, Chiran Mandula; Sivaraman, Anand; Vinekar, Anand

doi:10.3389/fped.2023.1197237

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…From this study it is observable that studies on ROP disease can be grouped into five classes: Detection of ROP stages [12], [32], [39], Detection of mild or severe ROP symptoms [10], [11], [36], detection of the Pre or Plus ROP disease [17], [30], [33], [34], [38], [41], Presence or absence of ROP [29], [37], [40] and ROP in zones [31], [35]. Studies investigating ROP at stage three and presented data, model design and results of the model accuracy was one [32].…”

Section: Discussionmentioning

confidence: 99%

“…Coyner et al [36] developed a logistic regression model for detecting whether an infant with ROP would require treatment and the data size set was of 3760 images collected from India, Nepal and Mongolia. Rao et al [37] developed a Deep Learning algorithm for ROP detection using 37,477 images captured using different devices in India and for a period of eleven years. 25,982 images were used to train the model, 4006 for validation and 7,489 for testing.…”

Section: B Architectures Designmentioning

confidence: 99%

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Retinopathy of Prematurity Disease Diagnosis using Deep Learning

Ndunge Mutua,

Shibwabo Kasamani,

Reich

2024

IJCDS

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Retinopathy of Prematurity (ROP) is a disease affecting infants born preterm, at birth their retina is not well developed and in most times after birth the veins of the retina do not develop to full term. Sometimes these veins stop growing and then suddenly start growing to the wrong directions and this abnormally causes retina traction, causing blindness. Each country has its own screening guidelines for the disease diagnosis. The disease can be categorized as severe or mild and has five stages. Stage one and two is not severe and can develop and heal unnoticed. Stage three should be diagnosed because it is reversable through treatment but when the disease progresses to Stage four, retina traction occurs causing blindness at stage five. The emergent of digital imaging support has resulted to having hospitals capturing retina images to determine the presence or absence of severe ROP. These images can be used to determine the presence of retinal detachment or lack of growth of the veins. ROP disease diagnosis is expensive with few eye specialists available in hospitals and the process of capturing retina images by non-eye specialists and transmitting them to specialists for diagnosis pauses many issues. Different cameras produce images of different contrast, image transmission may cause quality reduction depending on the channel of transmission. These challenges call for the development of systems to support both image quality assessment and assistive disease diagnosis. This paper proposes a Deep learning model to assist ophthalmologists to determine the presence or absence of the disease as well as diagnosing the disease at stage three. A customized ResNet-50 model was first applied to preprocess the images and separate images of quality from non-quality images. Ninety-one (91) images were obtained from Kaggle database and 11,100 images from HVDROPDB database which were used for model training, testing and validation at a ratio of 0.80 training, 0.20 testing, 0.20 validation. After preprocessing, desired features were extracted and fed into a Deep Neural Network to quickly classify them as either having the disease or not. Those with ROP were further divided into two sub-classes: ROP Stage two or three. The model was able to achieve an accuracy of 92.8%, sensitivity of 94.9%, and precision of 97.3%.

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Section: Discussionmentioning

confidence: 99%

Section: B Architectures Designmentioning

confidence: 99%

Retinopathy of Prematurity Disease Diagnosis using Deep Learning

Ndunge Mutua,

Shibwabo Kasamani,

Reich

2024

IJCDS

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“…Clinical informatics has been instrumental in developing algorithms that analyze retinal images to identify preterm infants at risk of ROP. By automating this process, clinicians can make quicker and more accurate decisions about when to initiate treatment, preventing vision loss in vulnerable neonates ( 18 – 20 ). Clinical informatics tools have also been employed to optimize workflows in the NICU.…”

Section: Incidence/prevalence Of Bpd and Why It Is A Priority For Imp...mentioning

confidence: 99%

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Moreira,

Husain,

Knake

et al. 2024

Front. Pediatr.

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Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

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Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence

Raja Sankari,

Umapathy

2024

Biomed. Phys. Eng. Express

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Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolution Neural Networks (CNN) based pre-trained networks. The characteristic demarcation lines and ridges in early stage ROP are segmented utilising a novel Swin U-Net. 2000 Scale Invariant Feature Transform (SIFT) descriptors were extracted from the segmented ridges and are dimensionally reduced to 50 features using Principal Component Analysis (PCA). Seven ROP-specific features, including six Gray Level Co-occurrence Matrix (GLCM) and ridge length features, are extracted from the segmented image and are fused with the PCA reduced 50 SIFT features. Finally, three ML classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and k- Nearest Neighbor (k-NN), are used to classify the 50 features to predict the early-stage ROP from Normal images. On the other hand, the raw retinal images are classified directly into normal and early-stage ROP using six pre-trained classifiers, namely ResNet50, ShuffleNet V2, EfficientNet, MobileNet, VGG16, and DarkNet19. It is seen that the ResNet50 network outperformed all other networks in predicting early-stage ROP with 89.5% accuracy, 87.5% sensitivity, 91.5% specificity, 91.1% precision, 88% NPV and an Area Under the Curve (AUC) of 0.92. Swin U-Net Convolutional Neural Networks (CNN) segmented the ridges and demarcation lines with an accuracy of 89.7% with 80.5% precision, 92.6% recall, 75.76% IoU, and 0.86 as the Dice coefficient. The SVM classifier using the 57 features from the segmented images achieved a classification accuracy of 88.75%, sensitivity of 90%, specificity of 87.5%, and an AUC of 0.91. The system can be utilised as a point-of-care diagnostic tool for ROP diagnosis of neonates in remote areas.

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Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Cited by 4 publications

References 44 publications

Retinopathy of Prematurity Disease Diagnosis using Deep Learning

Retinopathy of Prematurity Disease Diagnosis using Deep Learning

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence

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