The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks

Bai, Long; Chen, Sihang; Gao, Mingyang; Abdelrahman, Leila; Ghamdi, Manal Al; Abdel-Mottaleb, Mohamed

doi:10.1109/embc46164.2021.9629607

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In Experiment 2, poor image quality further reduced the reliability of the drusen mask in the ODIR dataset. The issue of poor image quality has also been mentioned by other works 38,39 . The pretrained drusen segmentation model that we used was trained on high‐quality CFPs with a resolution of 1200 × 1200 36 …”

Section: Classification Resultsmentioning

confidence: 84%

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Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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Introduction The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision. Methods A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods. Results Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). Conclusion The proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting.

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Section: Classification Resultsmentioning

confidence: 84%

“…The issue of poor image quality has also been mentioned by other works. 38 , 39 The pretrained drusen segmentation model that we used was trained on high‐quality CFPs with a resolution of 1200 × 1200. 36 …”

Section: Classification Resultsmentioning

confidence: 99%

Drusen‐aware model for age‐related macular degeneration recognition

Pan

et al. 2023

Ophthalmic Physiologic Optic

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“…[15,16] The availability of correct clinical information has been shown to improve the interpretations of diagnostic tests [17] accuracy of computerized tomography interpretation by radiologists, [18] and interpretation of radiological imaging [19] in addition to the impact of including age and gender in DR screening algorithms. [20] AI algorithms have been touted as a means of improving health care access in low resource settings.…”

Section: Background On Fundus Image Labeling and Use Of Metadata For ...mentioning

confidence: 99%

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa (Preprint)

Arunga,

Morley,

Kwaga

et al. 2024

Preprint

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BACKGROUND Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence (AI) screening algorithms for the detection of diabetic retinopathy. The International Classification of Diabetic Retinopathy (ICDR) is used to assign labels to CFP, plus the presence or absence of macular edema. Images can be grouped as referrable or non-referrable for treatment. There is little guidance in the literature about how to collect and use clinical metadata as a part of the CFP labeling process. OBJECTIVE To improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of clinical metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. METHODS This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and presence or absence of macular edema for each of 50 CRF in a test image with and without metadata. Specificity and sensitivity of referable retinopathy was based on ICDR scores, and macular edema calculated using 2-sided T-test. Comparison with and without metadata for each participant was calculated using the signed rank test. Statistical significance was set at P<0.05. RESULTS The sensitivity for identifying referrable diabetic retinopathy with metadata was 92.8% (95% CI: 87.6-98.0) compared with 93.3% (95% CI: 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI: 75.1-94.6) with metadata compared with 88.2% (95% CI: 79.5-96.8) without metadata. The sensitivity for identifying the presence of macular edema was 64.3% (95% CI: 57.6-71.0) with metadata, compared with 63.1% (95% CI: 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI: 81.4-91.5) with metadata compared with 87.7% (95% CI: 83.9-91.5) without metadata. Sensitivity and specificity of ICDR score and presence or absence of ME were also calculated for the 20 individual labelers with and without metadata. No findings were statistically significant. CONCLUSIONS In this quality improvement project, clinical metadata availability did not influence labeling quality. Additional studies are needed to understand the potential implications of the process and components of labeling with and without metadata more thoroughly with regards to accuracy and bias. These issues have far reaching implications given the rapidly expanding use of AI with clinical images, including on the African continent.

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“…In this circumstance, other compensating sensors like GSR should activate to detect the stress and pain condition for pain report. Machine learning techniques ( Bai et al, 2021 ; Bai et al, 2022 ) may also be employed to help the system learn the pain feature of particular patients, enabling more real-time feedback when the pain features appear in patients’ daily activities.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Rethinking pain communication of patients with Alzheimer’s disease through E-textile interaction design

Li,

Bai,

Mao

et al. 2023

Front. Physiol.

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Older individuals are easily prone to chronic pain. Due to the complexity of chronic pain, most elderly often have difficulty expressing pain to others to seek assistance, especially those with Alzheimer’s disease (AD). The caregivers cannot instantly discover the patients’ pain condition and provide timely pain management. This project applies physiological signal sensing technology to help AD patients express the presence of pain non-verbally. We embed sensors on patients’ handkerchiefs to identify the patient’s abnormal physical activity when pain occurs. Next, we translate the physiological signal into qualitative light alert to send to caregivers and indicate the pain occurrence condition. Then, utilizing multi-sensory stimulation intervention, we create an electronic textile (e-textile) tool to help caregivers effectively support patients in pain. And thus to create a two-way pain communication between caregivers and the patients. Pain perception can be independent of subjective expressions and tangibly perceived by others through our textile prototype. The e-textile handkerchiefs also bring up a new guide to facilitate communication for caregivers when their patients. We contribute the design insights of building a bio-sensing and e-textile system with considering the pain communication needs, patients’ pain behaviors and preference of objects. Our e-textile system may contribute to pain communication bio-sensing tool design for special elderly groups, especially those with weakened cognition and communication abilities. We provide a new approach to dealing with the pain of AD patients for healthcare professionals.

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The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks

Cited by 10 publications

References 18 publications

Drusen‐aware model for age‐related macular degeneration recognition

Drusen‐aware model for age‐related macular degeneration recognition

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa (Preprint)

Rethinking pain communication of patients with Alzheimer’s disease through E-textile interaction design

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