Imaging of Soft Tissue Infections.

Although superficial infections can often be diagnosed and managed clinically, physical examination may lack sensitivity and specificity, and imaging is often required to evaluate the depth of involvement and identify complications. Depending on the area of involvement, radiography, ultrasound, CT, MR imaging, or a combination of imaging modalities may be required. Soft tissue infections can be nonnecrotizing or necrotizing, with the later having a morbid and rapid course. Infectious tenosynovitis most commonly affects the flexor tendon sheaths of the hand, characterized by thickened and enhancing synovium with fluid-filled tendon sheaths.

Transmigración de canino mandibular: etiología, manejo quirúrgico y consideraciones especiales / Mandibular canine transmigration: etiology, surgical management and special considerations

En raras ocasiones, el canino mandibular derecho o izquierdo se coloca en el lado opuesto al habitual. Esta perturbación se define como la transmigración. Existen diversas teorías de su etiología, así como factores que la condicionan. La transmigración mandibular es un término que no está descrito en la literatura contemporánea y son pocos los casos reportados a nivel mundial. Presentamos un caso de trasmigración de canino mandibular derecho, posicionado por debajo del agujero mentoniano de lado izquierdo, cerca del borde basal mandibular, el cual se extrajo bajo anestesia general. Presentamos la etiología, técnica quirúrgica y consideraciones especiales en casos de trasmigración de canino mandibular (AU)

In rare occasions right or left mandibular canine is positionated at opposed side of habitual. This disturbance is defined as transmigration. There exist diverse theories about its etiology as well as conditioning factors. Mandibular transmigration is a non described term in modern literature and there are only a few reported cases at world level. We present one case of right canine transmigration positionated intimately below of left side mentonian hole near of mandibular basal edge which it was extracted under general anesthesia. We present also the etiology, surgical technique and special considerations of mandibular canine transmigration cases (AU)

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'1-5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6-10. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data11-15. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework16. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were obtained. The linguistic complexity of these reports was compared with that of alternative corpora. Head CT reports were preprocessed, and machine-analyzable features were constructed by using bag-of-words (BOW), word embedding, and Latent Dirichlet allocation-based approaches. Ultimately, 1004 head CT reports were manually labeled for findings of interest by physicians, and a subset of these were deemed critical findings. Lasso logistic regression was used to train models for physician-assigned labels on 602 of 1004 head CT reports (60%) using the constructed features, and the performance of these models was validated on a held-out 402 of 1004 reports (40%). Models were scored by area under the receiver operating characteristic curve (AUC), and aggregate AUC statistics were reported for (a) all labels, (b) critical labels, and (c) the presence of any critical finding in a report. Sensitivity, specificity, accuracy, and F1 score were reported for the best performing model's (a) predictions of all labels and (b) identification of reports containing critical findings. Results The best-performing model (BOW with unigrams, bigrams, and trigrams plus average word embeddings vector) had a held-out AUC of 0.966 for identifying the presence of any critical head CT finding and an average 0.957 AUC across all head CT findings. Sensitivity and specificity for identifying the presence of any critical finding were 92.59% (175 of 189) and 89.67% (191 of 213), respectively. Average sensitivity and specificity across all findings were 90.25% (1898 of 2103) and 91.72% (18 351 of 20 007), respectively. Simpler BOW methods achieved results competitive with those of more sophisticated approaches, with an average AUC for presence of any critical finding of 0.951 for unigram BOW versus 0.966 for the best-performing model. The Yule I of the head CT corpus was 34, markedly lower than that of the Reuters corpus (at 103) or I2B2 discharge summaries (at 271), indicating lower linguistic complexity. Conclusion Automated methods can be used to identify findings in radiology reports. The success of this approach benefits from the standardized language of these reports. With this method, a large labeled corpus can be generated for applications such as deep learning. © RSNA, 2018 Online supplemental material is available for this article.

Large Subpectoral Lipoma on Screening Mammography.

A 61 year-old woman presenting for bilateral screening mammogram was found to have an oval fat-density mass in the posterior right breast, partially visualized, with anterior displacement and thinning of the pectoralis major muscle. This mass was found on CT and MRI correlation to represent a large fat-containing mass, likely a lipoma, deep to the pectoralis major. On subsequent screening mammograms, the visualized portion of the mass remained stable. Subpectoral lipomas and intramuscular lipomas within the pectoralis major are rare, and their appearance on mammography may not be familiar to most radiologists. A review of the literature and a discussion of their appearance on multiple imaging modalities is provided.