Purpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,” “deep learning,” “neural network,” “renal cell carcinoma,” “kidney cancer,” “urothelial carcinoma,” “bladder cancer,” “prostate cancer,” and “robotic surgery.” Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. Conclusions AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Purpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,” “deep learning,” “neural network,” “renal cell carcinoma,” “kidney cancer,” “urothelial carcinoma,” “bladder cancer,” “prostate cancer,” and “robotic surgery.” Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. Conclusions AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Purpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,” “deep learning,” “neural network,” “renal cell carcinoma,” “kidney cancer,” “urothelial carcinoma,” “bladder cancer,” “prostate cancer,” and “robotic surgery.” Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. Conclusions AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Purpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,” “deep learning,” “neural network,” “renal cell carcinoma,” “kidney cancer,” “urothelial carcinoma,” “bladder cancer,” “prostate cancer,” and “robotic surgery.” Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. Conclusions AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Background: This study aimed to examine the association of serum uric acid levels with incident cardiovascular disease and mortality in Korean adults without gout. Methods: This large longitudinal cohort study included adults aged > 19 years who had serum uric acid levels measured at least once at the National Health Insurance Service Ilsan Hospital from January 1, 2006 to December 31, 2015. Longitudinal data on person-level cardiovascular disease and cardiovascular mortality were linked to the National Health Insurance Service claims database and National Death Index. Results: Among a total of 92,454 study participants with a median follow-up of 4.7 years, 7,670 (8.3%) composite events of cardiovascular disease or cardiovascular mortality were observed. Multivariable Cox proportional-hazards models revealed that each 1 mg/dL increment in uric acid level was associated with a 6% higher risk of composite outcomes.Compared with that for the uric acid level category of 4.0 to < 5.0 mg/dL, adjusted hazard ratios (95% confidence interval) for uric acid level categories of 5.0 to < 6.0, 6.0 to < 7.0, and ≥ 7.0 mg/dL were 1.10 (1.04–1.18), 1.20 (1.11–1.30), and 1.36 (1.25–1.47), respectively. In the secondary analyses for cardiovascular disease or cardiovascular mortality examined separately, a higher uric acid level was similarly associated with a higher risk of each adverse outcome. These associations were generally consistent across clinically relevant subgroups. Conclusion A graded association was noted between serum uric acid levels and cardiovascular risk, suggesting that higher uric acid levels may adversely affect cardiovascular health and survival in individuals without gout.

Background: There is limited information on the clinical characteristics of patients with coronavirus disease 2019 (COVID-19) who are asymptomatic or have mild symptoms. Methods: We performed a retrospective case series of patients with COVID-19 enrolled from February 22 to March 26, 2020. Forty cases of COVID-19 were confirmed using real-time reverse-transcription polymerase chain reaction among patients who underwent screening tests and were consecutively hospitalized at Ulsan University Hospital, Ulsan, Korea.The final follow-up date was May 19, 2020. All COVID-19 cases in Ulsan were included.Demographic and epidemiological information, comorbidities, clinical signs and symptoms, laboratory and radiologic findings, medications, treatments, outcomes, and main durations of patients with COVID-19 were compared according to supplemental oxygen requirement. Results: Forty patients were included (median age, 30 years; interquartile range [IQR], 25–57 years; 58% female). Six (15%) patients required supplemental oxygen. The prevalence of asymptomatic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection was 5% and that of presymptomatic infection was 13%. Cough, fever, myalgia, rhinorrhea or nasal congestion, and diarrhea were the screening criteria for diagnosing symptomatic and presymptomatic SARS-CoV-2 infections. Sputum production, chest discomfort, a large number of symptoms, abnormal procalcitonin and C-reactive protein levels, and abnormal chest X-ray or chest computed tomography findings were more common in patients requiring supplemental oxygen than in those not requiring supplemental oxygen. Overall mortality rate was 3% (1/40). Four patients (10%) were readmitted after testing positive by reversetranscription polymerase chain reaction again. Incubation period was 5 days (IQR, 4–6 days), and the duration of viral shedding was 21 days (IQR, 14–28 days; maximum, 51 days). Conclusion The prevalence of asymptomatic SARS-CoV-2 infection was 5%, which is much lower than that previously reported. This finding suggests that careful interviews and follow-ups should be performed to identify SARS-CoV-2 infections. Cough, fever, myalgia, rhinorrhea or nasal congestion, and diarrhea are adequate screening criteria for covering all symptoms of SARS-CoV-2 infection. Further evaluation is required to create representative screening criteria for COVID-19.

Objectives: Lack of physical activity has a critical effect on the physical and mental health of adolescents. This study examined the influence of family adversities on the longitudinal changes in physical inactivity among adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The study used multi-wave data from the Korean Children and Youth Panel Survey, including 2590 Korean adolescents aged 12-14 years. The longitudinal trajectory of physical inactivity among adolescents and the effects of related factors were estimated using a latent growth modeling method. Results: Our results revealed a significant increase in physical inactivity among adolescents over time. At the onset of the pandemic, approximately one-seventh of Korean middle schoolers reported a lack of physical activity. However, 3 years later, during the quarantine, nearly one-fifth of these adolescents reported a significant increase in their physical inactivity. Initially, low level parental education was predictive of adolescents’ physical inactivity, but this effect diminished over time, becoming statistically insignificant by the end of the 3-year period. Moreover, the increase in physical inactivity over the 3 years was significantly influenced by parental rejection. Conclusions These findings suggest that adolescents who experience parental rejection are more likely to report an increase in sedentary behaviors in contexts such as the COVID-19 pandemic.

Objectives: Lack of physical activity has a critical effect on the physical and mental health of adolescents. This study examined the influence of family adversities on the longitudinal changes in physical inactivity among adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The study used multi-wave data from the Korean Children and Youth Panel Survey, including 2590 Korean adolescents aged 12-14 years. The longitudinal trajectory of physical inactivity among adolescents and the effects of related factors were estimated using a latent growth modeling method. Results: Our results revealed a significant increase in physical inactivity among adolescents over time. At the onset of the pandemic, approximately one-seventh of Korean middle schoolers reported a lack of physical activity. However, 3 years later, during the quarantine, nearly one-fifth of these adolescents reported a significant increase in their physical inactivity. Initially, low level parental education was predictive of adolescents’ physical inactivity, but this effect diminished over time, becoming statistically insignificant by the end of the 3-year period. Moreover, the increase in physical inactivity over the 3 years was significantly influenced by parental rejection. Conclusions These findings suggest that adolescents who experience parental rejection are more likely to report an increase in sedentary behaviors in contexts such as the COVID-19 pandemic.

Objectives: Lack of physical activity has a critical effect on the physical and mental health of adolescents. This study examined the influence of family adversities on the longitudinal changes in physical inactivity among adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The study used multi-wave data from the Korean Children and Youth Panel Survey, including 2590 Korean adolescents aged 12-14 years. The longitudinal trajectory of physical inactivity among adolescents and the effects of related factors were estimated using a latent growth modeling method. Results: Our results revealed a significant increase in physical inactivity among adolescents over time. At the onset of the pandemic, approximately one-seventh of Korean middle schoolers reported a lack of physical activity. However, 3 years later, during the quarantine, nearly one-fifth of these adolescents reported a significant increase in their physical inactivity. Initially, low level parental education was predictive of adolescents’ physical inactivity, but this effect diminished over time, becoming statistically insignificant by the end of the 3-year period. Moreover, the increase in physical inactivity over the 3 years was significantly influenced by parental rejection. Conclusions These findings suggest that adolescents who experience parental rejection are more likely to report an increase in sedentary behaviors in contexts such as the COVID-19 pandemic.

Objectives: Lack of physical activity has a critical effect on the physical and mental health of adolescents. This study examined the influence of family adversities on the longitudinal changes in physical inactivity among adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: The study used multi-wave data from the Korean Children and Youth Panel Survey, including 2590 Korean adolescents aged 12-14 years. The longitudinal trajectory of physical inactivity among adolescents and the effects of related factors were estimated using a latent growth modeling method. Results: Our results revealed a significant increase in physical inactivity among adolescents over time. At the onset of the pandemic, approximately one-seventh of Korean middle schoolers reported a lack of physical activity. However, 3 years later, during the quarantine, nearly one-fifth of these adolescents reported a significant increase in their physical inactivity. Initially, low level parental education was predictive of adolescents’ physical inactivity, but this effect diminished over time, becoming statistically insignificant by the end of the 3-year period. Moreover, the increase in physical inactivity over the 3 years was significantly influenced by parental rejection. Conclusions These findings suggest that adolescents who experience parental rejection are more likely to report an increase in sedentary behaviors in contexts such as the COVID-19 pandemic.