Residual liver function is the major factor influencing the outcome of patients after hepatectomy and can be predicted preoperatively by a combination of LiMAx and CT volumetry.

The LiMAx test can validly determine liver function capacity and is feasible in every clinical situation. Combination with virtual resection could enable the calculation of residual liver function. The LiMAx decision tree algorithm for hepatectomy might significantly improve preoperative evaluation and postoperative outcome in liver surgery.

Background and aims Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity. Methods Thirty patients with COVID-19 and a mean age of 65.6 ± 13.1 years from a level-one medical center in Berlin, Germany, were included in the present cross-sectional analysis. COVID-19 was confirmed by polymerase chain reaction (PCR) from nasal and throat swabs. A severe clinical course of COVID-19 was defined by hospitalization in the intensive care unit (ICU) and/or invasive mechanical ventilation. Fat was measured at the level of the first lumbar vertebra on routinely acquired low-dose chest computed tomography (CT). Results An increase in visceral fat area (VFA) by ten square centimeters was associated with a 1.37-fold higher likelihood of ICU treatment and a 1.32-fold higher likelihood of mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference was associated with a 1.13-fold higher likelihood of ICU treatment and a 1.25-fold higher likelihood of mechanical ventilation. Conclusions Our proof-of-concept study suggests that visceral adipose tissue and upper abdominal circumference specifically increase the likelihood of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routine chest CTs may therefore be a simple tool for risk assessment in COVID-19 patients.

By using two equations developed from the data obtained in this study, expected intraoperative weight and volume can properly be determined from CT volumetric measurements.

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware. Chest radiographs are among the most frequently used imaging procedures in radiology. They have been widely employed in the field of computer vision, as chest radiographs are a standardized technique and, if compared to other radiological examinations such as computed tomography or magnetic resonance imaging, contain a smaller group of relevant pathologies. Although many artificial neural networks for the classification of chest radiographs have been developed, it is still subject to intensive research. Only a few groups design their own networks from scratch, while most use already established architectures, such as ResNet-50 or DenseNet-121 (with 50 and 121 representing the number of layers within the respective neural network) 1-6. These neural networks have often been trained on large, openly available datasets, such as ImageNet, and are therefore already able to recognize numerous image features. When training a model for a new task, such as the classification of chest radiographs, the use of pre-trained networks may improve the training speed and accuracy of the new model, since important image features that have already been learned can be transferred to the new task and do not have to be learned again. However, the feature space of freely available data sets such as ImageNet differs from chest radiographs as they contain color images and more categories. The ImageNet Challenge includes 1,000 possible categories per image, while CheXpert, a large freely available data set of chest radiographs, only distinguishes between 14 categories (or classes) 7 , and the COVID-19 Image Data Collection only differentiates between three classes 8. Although the ImageNet...

Partial hepatectomy leads to fast and complete functional recovery, while volume recovery is delayed and remains often incomplete. The functional recovery is mainly influenced by the preoperative liver function, the residual liver volume, and by obesity.

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 (±0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 (±0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 (±0.04), and an F1 score of 0.58 (±0.04) corresponding to a PPV of 0.67 (±0.05) and TPR of 0.51 (±0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

PurposeVolumetric assessment of the liver regularly yields discrepant results between pre- and intraoperatively determined volumes. Nevertheless, the main factor responsible for this discrepancy remains still unclear. The aim of this study was to systematically determine the difference between in vivo CT-volumetry and ex vivo volumetry in a pig animal model.Material and MethodsEleven pigs were studied. Liver density assessment, CT-volumetry and water displacement volumetry was performed after surgical removal of the complete liver. Known possible errors of volume determination like resection or segmentation borders were eliminated in this model. Regression analysis was performed and differences between CT-volumetry and water displacement determined.ResultsMedian liver density was 1.07 g/ml. Regression analysis showed a high correlation of r2 = 0.985 between CT-volumetry and water displacement. CTvolumetry was found to be 13% higher than water displacement volumetry (p < 0.0001).ConclusionIn this study the only relevant factor leading to the difference between in vivo CT-volumetry and ex vivo water displacement volumetry seems to be blood perfusion of the liver. The systematic difference of 13 percent has to be taken in account when dealing with those measures.