Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.

BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study interpretation, which can be a challenge on busy emergency radiology services. A machine learning system has the potential to automate the process, potentially leading to a faster clinical response. This study aimed to create such a system. METHOD: Using the American Association for the Surgery of Trauma (AAST), spleen injuries were classified into 3 classes: normal, low-grade (AAST grade I-III) injuries, and high-grade (AAST grade IV and V) injuries. Employing a 2-stage machine learning strategy, spleens were initially segmented from input CT images and subsequently underwent classification via a 3D dense convolutional neural network (DenseNet). RESULTS: This single-centre retrospective study involved trauma protocol CT scans performed between January 1, 2005, and July 31, 2021, totaling 608 scans with splenic injuries and 608 without. Five board-certified fellowship-trained abdominal radiologists utilizing the AAST injury scoring scale established ground truth labels. The model achieved AUC values of 0.84, 0.69, and 0.90 for normal, low-grade injuries, and high-grade splenic injuries, respectively. CONCLUSIONS: Our findings demonstrate the feasibility of automating spleen injury detection using our method with potential applications in improving patient care through radiologist worklist prioritization and injury stratification. Future endeavours should concentrate on further enhancing and optimizing our approach and testing its use in a real-world clinical environment.

Delayed Gadolinium Leakage in Ocular Structures on Brain MR Imaging: Prevalence and Associated Factors.

BACKGROUND AND PURPOSE: Gadolinium leakage in ocular structures (GLOS) is characterized by hyperintense signal in the chambers of the eye on FLAIR and has been reported in association with blood-ocular barrier breakdown in patients with ischemic strokes. The underlying mechanism of GLOS remains poorly understood; however, some studies suggest it may be part of a physiologic excretion pathway of gadolinium. This study aimed to determine the prevalence of GLOS in an unselected patient population. MATERIALS AND METHODS: A retrospective analysis was conducted on 439 patients who underwent brain MR imaging within 7 days of receiving a gadolinium-based contrast agent injection for a prior MR imaging study. Clinical, imaging, and laboratory data were collected. Descriptive and logistic regression analyses were performed. RESULTS: GLOS was observed in 26 of 439 patients (6%). The occurrence of GLOS varied with time, with 3 (12%), 14 (54%), 8 (31%), and 1 (4%) patient showing GLOS within 24, 25-72, 73-120, and >120 hours after gadolinium-based contrast agent injection, respectively. Patients with GLOS were older (median age: 72 versus 55 years, P = .001) and had higher median serum creatinine levels (73 versus 64 µmol/L, P = .005) and a lower median estimated glomerular filtration rate (84 versus 101 mL/min/1.73 m2, P < .001). A shorter median time interval between gadolinium-based contrast agent injection and the index brain MR imaging was observed in the group positive for GLOS (62 versus 91 hours, P = .003). Multivariable regression analysis identified the estimated glomerular filtration rate (OR = 0.970; 95% CI, 0.049-0.992; P = .008) and time interval since gadolinium-based contrast agent injection (OR = 0.987; 95% CI, 0.977-0.997; P = .012) as independent factors associated with GLOS. CONCLUSIONS: GLOS was observed in only a small percentage of patients receiving gadolinium-based contrast agent within 7 days before brain MR imaging. This phenomenon was noted in patients with normal findings on brain MR imaging and those with various CNS pathologies, and it was associated with lower estimated glomerular filtration rates and shorter time intervals after gadolinium-based contrast agent injection. While GLOS may be a physiologic gadolinium-based contrast agent excretion pathway, the presence of ocular disease was not formally evaluated in the included population. Awareness of GLOS is nonetheless useful for appropriate radiologic interpretation.

Tomografía computarizada con multidetectores en el diagnóstico de la estenosis coronaria: [revisión] / Computed tomography with multidetectors in the diagnosis of coronary stenosis: [review]

La angiografía coronaria que emplea tomografía multidetector, es una técnica no invasiva para la detección de estenosis coronaria, que ha tenido avances tecnológicos significativos en los últimos años. La introducción de tomógrafos de 16 y 64 detectores, la posibilidad de poder sincronizar la toma de imágenes con el electrocardiograma y las técnicas de reconstrucción permiten una mejor resolución temporal y espacial que logra la identificación de placas ateromatosas y lesiones obstructivas significativas a nivel de las arterias coronarias. En los próximos años continuará su maduración y se convertirá en una herramienta útil como técnica no invasiva para el diagnóstico de la enfermedad coronaria y se integrará de forma definitiva a los protocolos de manejo. La siguiente revisión se enfocará en los aspectos básicos de la tomografía, su técnica, su desempeño diagnóstico y sus aplicaciones clínicas.

Coronary angiography that uses multislice spiral computed tomography is a noninvasive technique for the detection of coronary stenosis that has had significant improvements in recent years. The introduction of 16 and 64 row scanners, the development of synchronized scanning electrocardiogram and better reconstruction techniques permit higher spatial and temporal resolution that allows better identification of coronary plaques and significant obstructive coronary lesions. In the next years Multislice spiral computed tomography will continue maturing and it will become a useful non invasive diagnostic imaging tool for the diagnosis of coronary disease and will be integrated to the cardiologic management protocols. Our next review will be focused on the basic and technical aspects of the scanner, diagnostic performance and clinical applications of this new technology.