A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and mortality. However, PE remains subject to misdiagnosis. METHODS: We retrospectively identified 251 CTPAs performed at a tertiary care hospital between January 2018 to January 2021. The scans were classified as positive (n = 55) and negative (n = 196) for PE based on the annotations made by board-certified radiologists. A fully anonymized CT slice served as input for the detection of PE by the 2D segmentation model comprising U-Net architecture with Xception encoder. The diagnostic performance of the model was calculated at both the scan and the slice levels. RESULTS: The model correctly identified 44 out of 55 scans as positive for PE and 146 out of 196 scans as negative for PE with a sensitivity of 0.80 [95% CI 0.68, 0.89], a specificity of 0.74 [95% CI 0.68, 0.80], and an accuracy of 0.76 [95% CI 0.70, 0.81]. On slice level, 4817 out of 5183 slices were marked as positive for the presence of emboli with a specificity of 0.89 [95% CI 0.88, 0.89], a sensitivity of 0.93 [95% CI 0.92, 0.94], and an accuracy of 0.89 [95% CI 0.887, 0.890]. The model also achieved an AUROC of 0.85 [0.78, 0.90] and 0.94 [0.936, 0.941] at scan level and slice level, respectively for the detection of PE. CONCLUSION: The development of an AI model and its use for the identification of pulmonary embolism will support healthcare workers by reducing the rate of missed findings and minimizing the time required to screen the scans.

Utility of Constructive Interference in Steady-State Sequence in Detecting Thin Pituitary Stalk in Pituitary Stalk Interruption Syndrome.

Pituitary stalk interruption syndrome (PSIS) is a rare congenital anomaly, causing hypothalamic-pituitary malfunction. It is characterized by hypoplastic anterior pituitary, absent or thin infundibulum, and absent or ectopic posterior pituitary. Its early recognition is important in disease management. MRI plays a pivotal role in early diagnosis. We report a case of a 13-year-old male child, presenting with stunting of growth and discrepancy between chronological and bone age of four years. A subsequent MRI revealed a small anterior pituitary, hypoplastic pituitary stalk, and an absence of visualization of the bright pituitary gland signal in the sella. The posterior pituitary gland was present ectopically in the midline along the floor of the third ventricle near the median eminence.