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.

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.

Dengue hemorrhagic fever is a severe form of dengue presenting commonly with bleeding diathesis, but rarely with peripheral nervous system manifestations. Proximal median neuropathy comprises 1% of upper limb compression syndromes, and this case is the first to report injury to the proximal median nerve due to compression from hematoma formation. This case report presents the rehabilitation process of a 25-year-old Filipino female median nerve entrapment from venipuncture-associated hematoma presenting as burning sensation on the medial elbow, forearm and hand, weak flexion movement of her left thumb, index, and middle fingers. The patient was managed conservatively with pain medications, range of motion, gross and fine motor, and sensory re-education exercises. At 12 months, there was partial but functional recovery of median motor distribution and full recovery of median sensory distribution, as evidenced by improved sensory nerve action potential conduction velocity and amplitude, and compound motor action potential conduction velocity, with persistent decreased amplitude at 50%, and decrease in cross-sectional area of the left median nerve.

This paper highlights the functional outcomes of a conservatively managed median nerve entrapment from venipuncture hematoma from dengue hemorrhagic fever. This case report also emphasizes that in the presence of severe bleeding risk of surgery in the background of severe thrombocytopenia, timely rehabilitation medicine referral with monitoring through clinical evaluation, musculoskeletal ultrasound, and electrodiagnostic study presents a viable alternative in the management of compression neuropathy.

Methods: Patients diagnosed with cervical degenerative spondylolisthesis were identified from a hospital’s medical records. Demographic and surgical characteristics were collected through a structured query language search and manual chart review. Radiographic measurements were made on preoperative MRIs for all vertebral levels diagnosed with spondylolisthesis and adjacent undiagnosed levels between C3 and C6. The facet fluid index was calculated by dividing the facet fluid measurement by the width of the facet. Bivariate analysis was conducted to compare facet characteristics based on radiographic spondylolisthesis and spondylolisthesis stability. Results: We included 154 patients, for whom 149 levels were classified as having spondylolisthesis and 206 levels did not. The average facet fluid index was significantly higher in patients with spondylolisthesis (0.26±0.07 vs. 0.23±0.08, p <0.001). In addition, both fluid width and facet width were significantly larger in patients with spondylolisthesis (p <0.001 each). Cervical levels in the fusion construct demonstrated a greater facet fluid index and were more likely to have unstable spondylolisthesis than stable spondylolisthesis (p <0.001 each). Conclusions Facet fluid index is associated with cervical spondylolisthesis and an increased facet size and fluid width are associated with unstable spondylolisthesis. While cervical spondylolisthesis continues to be an inconclusive finding, vertebral levels with spondylolisthesis, especially the unstable ones, were more likely to be included in the fusion procedure than those without spondylolisthesis.