The use of deep learning technology for the detection of optic neuropathy

Li, Mei; Wan, Chao

doi:10.21037/qims-21-728

Cited by 4 publications

(4 citation statements)

References 75 publications

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“…Healthcare is undergoing rapid digital transformation with the explosion of big data, advances in digital technology and the rise of artificial intelligence. Within ophthalmology, deep learning has shown robust performance such as in the detection of diabetic retinopathy, 110 glaucoma 111 and cataract 112 . In neuro‐ophthalmology, deep learning models have shown excellent performance in: (1) differentiating optic neuropathies from pseudopapilledema, 113,114 (2) differentiating demyelinating optic neuritis and non‐arteritic anterior ischemic optic neuropathy, 115 (3) differentiating glaucomatous optic neuropathy from non‐glaucomatous optic neuropathy, 116 all from optical coherence tomography or clinical fundal photographs.…”

Section: Future Directionsmentioning

confidence: 99%

Recent advances in the use of optical coherence tomography in neuro‐ophthalmology: A review

Ge,

Teo,

Loo

2024

Clinical Exper Ophthalmology

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Optical coherence tomography (OCT) is an in vivo imaging modality that provides non‐invasive, high resolution and fast cross‐sectional images of the optic nerve head, retina and choroid. OCT angiography (OCTA) is an emerging tool. It is a non‐invasive, dye‐free imaging approach of visualising the microvasculature of the retina and choroid by employing motion contrast imaging for blood flow detection and is gradually receiving attention for its potential roles in various neuro‐ophthalmic and retinal conditions. We will review the clinical utility of the OCT in the management of various common neuro‐ophthalmic and neurological disorders. We also review some of the OCTA research findings in these conditions. Finally, we will discuss the limitations of OCT as well as introduce other emerging technologies.

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Section: Future Directionsmentioning

confidence: 99%

Recent advances in the use of optical coherence tomography in neuro‐ophthalmology: A review

Ge,

Teo,

Loo

2024

Clinical Exper Ophthalmology

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“…The US examinations were performed by radiologists with more than 5 years of experience in US diagnosis using various commercially available units, such as DC-8 (Mindray, Shenzhen, China), Logic E9 (GE), HD15 (Philips, Best, The Netherlands), and IU22 (Philips), equipped with a highfrequency linear array probe (6)(7)(8)(9)(10)(11)(12)(13)(14).…”

Section: Us Image Acquisition and Filteringmentioning

confidence: 99%

“…Accumulating evidence suggests that this limitation can be overcome using artificial intelligence (AI) algorithms, particularly deep learning (DL), which is based on neural networks (NN) that mimic the human brain to identify patterns in huge datasets ( 11 , 12 ). Different DL architectures have been developed for different tasks, but convolutional neural networks (CNNs) are presently the most widespread DL architecture typology in medical imaging.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for the differential diagnosis of benign and malignant salivary gland tumors based on ultrasound imaging and clinical data

Zhang¹,

Li²,

Xu³

et al. 2023

Quant Imaging Med Surg

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Background:The preoperative differentiation between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs) is of great significance for therapeutic decision-making.Deep learning (DL), an artificial intelligence algorithm based on neural networks, can help overcome inconsistencies in conventional ultrasonic (CUS) examination outcomes. Therefore, as an auxiliary diagnostic tool, DL can support accurate diagnosis using massive ultrasonic (US) images. This current study developed and validated a DL-based US diagnosis for the preoperative differentiation of BPGT from MPGT.Methods: A total of 266 patients, including 178 patients with BPGT and 88 patients with MPGT, were consecutively identified from a pathology database and enrolled in this study. Ultimately, considering the limitations of the DL model, 173 patients were selected from the 266 patients and divided into 2 groups: a training set, and a testing set. US images of the 173 patients were used to construct the training set (including 66 benign and 66 malignant PGTs) and testing set (consisting of 21 benign and 20 malignant PGTs). These were then preprocessed by normalizing the grayscale of each image and reducing noise. Processed images were imported into the DL model, which was then trained to predict the images from the testing set and evaluated for performance. Based on the training and validation datasets, the diagnostic performance of the 3 models was assessed and verified using receiver operating characteristic (ROC) curves. Ultimately, before and after combining the clinical data, we compared the area under the curve (AUC) and diagnostic accuracy of the DL model with the opinions of trained radiologists to evaluate the application value of the DL model in US diagnosis.Results: The DL model showed a significantly higher AUC value compared to doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data (AUC =0.9583 vs. 0.6250, 0.7250, and 0.8025 respectively; all P<0.05). In addition, the sensitivity of the DL model was higher than the sensitivities of the doctors combined with clinical data (97.2% vs. 65%, 80%, and 90% for doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data, respectively; all P<0.05). Conclusions:The DL-based US imaging diagnostic model has excellent performance in differentiating BPGT from MPGT, supporting its value as a diagnostic tool for the clinical decision-making process.

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“…In recent years, with the interdisciplinary interpenetration of molecular biology, cytogenetics, genetic engineering, stem cell biology, and artificial intelligence ( 61 ), ophthalmologists’ knowledge and understanding of fundus diseases such as wAMD, diabetic macular edema, and macular hole have gradually improved, and their diagnosis and treatment have also advanced significantly. However, we still do not have effective treatment options for fundus diseases like retinitis pigmentosa and posterior scleral staphyloma in pathological myopia.…”

Section: Posterior Eye Segmentmentioning

confidence: 99%

Grand Challenges and Opportunities in Surgical Ophthalmology: Together for a Shared Future

et al. 2022

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The use of deep learning technology for the detection of optic neuropathy

Cited by 4 publications

References 75 publications

Recent advances in the use of optical coherence tomography in neuro‐ophthalmology: A review

Recent advances in the use of optical coherence tomography in neuro‐ophthalmology: A review

A deep learning model for the differential diagnosis of benign and malignant salivary gland tumors based on ultrasound imaging and clinical data

Grand Challenges and Opportunities in Surgical Ophthalmology: Together for a Shared Future

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