scholarly journals Appendicular Skeleton, Trunk, Skull, and Facial Bones Cancer pT2 TNM Finding v8

2020 ◽  
Author(s):  
Author(s):  
Gustavo A Ballen ◽  
Mario C C De Pinna

Abstract A standardized terminology for the anatomy of pectoral- and dorsal-fin spines in the order Siluriformes is proposed based on an extensive literature review and direct examination of representatives of the order. The adult anatomy of the spines is described in detail. Terminology of various spine parts are reviewed and standardized, each term provided with a synonymic list organizing previous usage. Most of the structures treated have been recorded and named in the literature, but some are herein named for the first time. A quantitative approach is proposed for orienting decisions on name usage, aiming at minimizing differences between the terminology proposed and the vast amount of pre-existing literature, herein called the cost function. It is expected that this system will aid efforts in organizing the chaotic anatomical nomenclature of the appendicular skeleton in Siluriformes, and provide a solid basis for advances in comparative anatomy and nomenclature. The proposed terminology system has potential application on a number of fields that utilize information from catfish spines, ranging from taxonomy to phylogenetic systematics to paleontology and archaeology.


2021 ◽  
Vol 7 (7) ◽  
pp. 105
Author(s):  
Guillaume Reichert ◽  
Ali Bellamine ◽  
Matthieu Fontaine ◽  
Beatrice Naipeanu ◽  
Adrien Altar ◽  
...  

The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.


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