Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Background/Aims: Alcohol represents a leading burden of disease worldwide, including alcohol use disorder (AUD) and alcohol-related liver disease (ALD). We aim to assess the global burden of AUD, ALD, and alcohol-attributable primary liver cancer between 2000–2021. Methods: We registered the global and regional trends of AUD, ALD, and alcohol-related liver cancer using data from the Global Burden of Disease 2021 Study, the largest and most up-to-date global epidemiology database. We estimated the annual percent change (APC) and its 95% confidence interval (CI) to assess changes in age-standardized rates over time. Results: In 2021, there were 111.12 million cases of AUD, 3.02 million cases of ALD, and 132,030 cases of alcohol-attributable primary liver cancer. Between 2000 and 2021, there was a 14.66% increase in AUD, a 38.68% increase in ALD, and a 94.12% increase in alcohol-attributable primary liver cancer prevalence. While the age-standardized prevalence rate for liver cancer from alcohol increased (APC 0.59%; 95% confidence interval [CI] 0.52 to 0.67%) over these years, it decreased for ALD (APC –0.71%; 95% CI –0.75 to –0.67%) and AUD (APC –0.90%; 95% CI –0.94 to –0.86%). There was significant variation by region, socioeconomic development level, and sex. During the last years (2019–2021), the prevalence, incidence, and death of ALD increased to a greater extent in females. Conclusions Given the high burden of AUD, ALD, and alcohol-attributable primary liver cancer, urgent measures are needed to prevent them at both global and national levels.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Background/Aims: Alcohol represents a leading burden of disease worldwide, including alcohol use disorder (AUD) and alcohol-related liver disease (ALD). We aim to assess the global burden of AUD, ALD, and alcohol-attributable primary liver cancer between 2000–2021. Methods: We registered the global and regional trends of AUD, ALD, and alcohol-related liver cancer using data from the Global Burden of Disease 2021 Study, the largest and most up-to-date global epidemiology database. We estimated the annual percent change (APC) and its 95% confidence interval (CI) to assess changes in age-standardized rates over time. Results: In 2021, there were 111.12 million cases of AUD, 3.02 million cases of ALD, and 132,030 cases of alcohol-attributable primary liver cancer. Between 2000 and 2021, there was a 14.66% increase in AUD, a 38.68% increase in ALD, and a 94.12% increase in alcohol-attributable primary liver cancer prevalence. While the age-standardized prevalence rate for liver cancer from alcohol increased (APC 0.59%; 95% confidence interval [CI] 0.52 to 0.67%) over these years, it decreased for ALD (APC –0.71%; 95% CI –0.75 to –0.67%) and AUD (APC –0.90%; 95% CI –0.94 to –0.86%). There was significant variation by region, socioeconomic development level, and sex. During the last years (2019–2021), the prevalence, incidence, and death of ALD increased to a greater extent in females. Conclusions Given the high burden of AUD, ALD, and alcohol-attributable primary liver cancer, urgent measures are needed to prevent them at both global and national levels.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results. Methods: This retrospective study included patients with a BI-RADS category 3 (i.e., probably benign) breast ultrasound assessment from August 2019–December 2019 whose follow-up was delayed during the COVID-19 pandemic and whose breast ultrasounds were re-reviewed using Koios DS Breast AI as part of the clinical workflow for triaging these patients. The output of Koios DS was compared with the true outcome of a presence or absence of breast cancer defined by resolution/stability on imaging follow-up for at least 2 years or pathology results. Results: The study included 161 women (mean age, 52 years) with 221 BI-RADS category 3 sonographic lesions. Of the 221 lesions, there were two confirmed cancers (0.9% malignancy rate). Koios DS assessed 112/221 lesions (50.7%) as benign, 42/221 lesions (19.0%) as probably benign, 64/221 lesions (29.0%) as suspicious, and 3/221 lesions (1.4%) as probably malignant. Koios DS had a sensitivity of 100% (2/2; 95% confidence interval [CI], 16% to 100%), specificity of 70% (154/219; 95% CI, 64% to 76%), negative predictive value of 100% (154/154; 95% CI, 98% to 100%), and false-positive rate of 30% (65/219; 95% CI, 24% to 36%). Conclusion When many follow-up appointments are delayed, e.g., natural disaster, or scenarios where resources are limited, breast ultrasound AI DS can help triage patients with probably benign breast ultrasounds.

Background/Aims: Alcohol represents a leading burden of disease worldwide, including alcohol use disorder (AUD) and alcohol-related liver disease (ALD). We aim to assess the global burden of AUD, ALD, and alcohol-attributable primary liver cancer between 2000–2021. Methods: We registered the global and regional trends of AUD, ALD, and alcohol-related liver cancer using data from the Global Burden of Disease 2021 Study, the largest and most up-to-date global epidemiology database. We estimated the annual percent change (APC) and its 95% confidence interval (CI) to assess changes in age-standardized rates over time. Results: In 2021, there were 111.12 million cases of AUD, 3.02 million cases of ALD, and 132,030 cases of alcohol-attributable primary liver cancer. Between 2000 and 2021, there was a 14.66% increase in AUD, a 38.68% increase in ALD, and a 94.12% increase in alcohol-attributable primary liver cancer prevalence. While the age-standardized prevalence rate for liver cancer from alcohol increased (APC 0.59%; 95% confidence interval [CI] 0.52 to 0.67%) over these years, it decreased for ALD (APC –0.71%; 95% CI –0.75 to –0.67%) and AUD (APC –0.90%; 95% CI –0.94 to –0.86%). There was significant variation by region, socioeconomic development level, and sex. During the last years (2019–2021), the prevalence, incidence, and death of ALD increased to a greater extent in females. Conclusions Given the high burden of AUD, ALD, and alcohol-attributable primary liver cancer, urgent measures are needed to prevent them at both global and national levels.