Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing. Methods: We developed novel urine test strips and an AI algorithm for image capture.Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters. Results: In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping. Conclusions The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.

Lecanemab (product name Leqembi ® ) is an anti-amyloid monoclonal antibody treatment approved for use in Korea for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease. The Korean Dementia Association has created recommendations for the appropriate use of lecanemab to assist clinicians. These recommendations include selecting patients for administration, necessary pre-administration tests and preparations,administration methods, monitoring for amyloid related imaging abnormalities (ARIA), and communication with patients and caregivers. Lecanemab is recommended for patients with MCI or mild dementia who confirmed positive amyloid biomarkers, and should not be administered to patients with severe hypersensitivity to lecanemab or those unable to undergo magnetic resonance imaging (MRI) evaluation. To predict the risk of ARIA before administration, apolipoprotein E genotyping is conducted, and regular brain MRI evaluations are recommended to monitor for ARIA during treatment. The most common adverse reactions are infusion-related reactions, which require appropriate management upon occurrence. Additional caution is needed when co-administering with anticoagulants or tissue plasminogen activator due to the risk of macrohemorrhage. Clinicians should consider the efficacy and necessary conditions for administration, as well as the safety of lecanemab, to make a comprehensive decision regarding its use.

Lecanemab (product name Leqembi ® ) is an anti-amyloid monoclonal antibody treatment approved for use in Korea for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease. The Korean Dementia Association has created recommendations for the appropriate use of lecanemab to assist clinicians. These recommendations include selecting patients for administration, necessary pre-administration tests and preparations,administration methods, monitoring for amyloid related imaging abnormalities (ARIA), and communication with patients and caregivers. Lecanemab is recommended for patients with MCI or mild dementia who confirmed positive amyloid biomarkers, and should not be administered to patients with severe hypersensitivity to lecanemab or those unable to undergo magnetic resonance imaging (MRI) evaluation. To predict the risk of ARIA before administration, apolipoprotein E genotyping is conducted, and regular brain MRI evaluations are recommended to monitor for ARIA during treatment. The most common adverse reactions are infusion-related reactions, which require appropriate management upon occurrence. Additional caution is needed when co-administering with anticoagulants or tissue plasminogen activator due to the risk of macrohemorrhage. Clinicians should consider the efficacy and necessary conditions for administration, as well as the safety of lecanemab, to make a comprehensive decision regarding its use.

Lecanemab (product name Leqembi ® ) is an anti-amyloid monoclonal antibody treatment approved for use in Korea for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease. The Korean Dementia Association has created recommendations for the appropriate use of lecanemab to assist clinicians. These recommendations include selecting patients for administration, necessary pre-administration tests and preparations,administration methods, monitoring for amyloid related imaging abnormalities (ARIA), and communication with patients and caregivers. Lecanemab is recommended for patients with MCI or mild dementia who confirmed positive amyloid biomarkers, and should not be administered to patients with severe hypersensitivity to lecanemab or those unable to undergo magnetic resonance imaging (MRI) evaluation. To predict the risk of ARIA before administration, apolipoprotein E genotyping is conducted, and regular brain MRI evaluations are recommended to monitor for ARIA during treatment. The most common adverse reactions are infusion-related reactions, which require appropriate management upon occurrence. Additional caution is needed when co-administering with anticoagulants or tissue plasminogen activator due to the risk of macrohemorrhage. Clinicians should consider the efficacy and necessary conditions for administration, as well as the safety of lecanemab, to make a comprehensive decision regarding its use.

Lecanemab (product name Leqembi ® ) is an anti-amyloid monoclonal antibody treatment approved for use in Korea for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease. The Korean Dementia Association has created recommendations for the appropriate use of lecanemab to assist clinicians. These recommendations include selecting patients for administration, necessary pre-administration tests and preparations,administration methods, monitoring for amyloid related imaging abnormalities (ARIA), and communication with patients and caregivers. Lecanemab is recommended for patients with MCI or mild dementia who confirmed positive amyloid biomarkers, and should not be administered to patients with severe hypersensitivity to lecanemab or those unable to undergo magnetic resonance imaging (MRI) evaluation. To predict the risk of ARIA before administration, apolipoprotein E genotyping is conducted, and regular brain MRI evaluations are recommended to monitor for ARIA during treatment. The most common adverse reactions are infusion-related reactions, which require appropriate management upon occurrence. Additional caution is needed when co-administering with anticoagulants or tissue plasminogen activator due to the risk of macrohemorrhage. Clinicians should consider the efficacy and necessary conditions for administration, as well as the safety of lecanemab, to make a comprehensive decision regarding its use.