More and more scholars regard the lesion of liver cirrhosis as a series of progressive clinical stages. Besides, liver cirrhosis is a late stage of scarring (fibrosis) of the liver, and has long been a common cause of death for the global adult population, thus, the treatment of liver cirrhosis is a key point investigated in the biomedical field. Here, we propose a novel hypothesis; if decellularized liver matrix (DLM) is possible injected into the injurant‐induced fiberized liver via the hepatic portal vein to thoroughly repair the liver, it may be an effective therapeutic method for liver fibrosis or even liver cirrhosis. This study mixed rat DLM with gelatin‐hydroxyphenylpropionic acid (Glt‐HPA) to form a three‐dimensional structure to simulate the in vivo liver environment, and cultured the primary rat hepatocytes in it. Afterward, the hepatocytes were treated using D‐galactosamine (GaIN), CHCl3, and CCl4‐containing medium to simulate the toxin‐mediated liver fibrosis in vitro. Finally, they were cultured in a DLM‐containing medium to observe the viability and functions of the damaged hepatocytes, and the hypothesis of this research was proved, meaning that Glt‐HPA‐DLM acting on damaged hepatocytes may repair them. Results have shown that Glt‐HPA‐DLM was effective for hepatocytes culture and repaired injured hepatocytes from GaIN, CHCl3, and CCl4 (albumin synthesis was increased by 219, 108, and 12%, respectively, whereas relative lactate dehydrogenase activity was reduced by 38, 68, and 67%, after 5 days of culture, separately). This research shows promising effects against hepatic fibrosis and may have potential for liver cirrhosis in vivo.

Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients’ morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30–70 mm, (ii) 30–60 mm, (iii) 20–60 mm, and (iv) 20–55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20–55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians’ consensus.

Background Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning, however, poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods. Purpose A deep learning–based approach to automatic beam angle selection is proposed for the proton pencil‐beam scanning treatment planning of liver lesions. Methods We cast beam‐angle selection as a multi‐label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance–based regularization and multi‐label circular–smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners’ clinical practice is employed in post‐processing to improve the output of the model. Forty‐nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI‐selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge‐based treatment planning. Results For 7 of the 11 cases in the test set, AI‐selected beam angles agreed with those chosen by human planners to within 20° (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10° of the human‐selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI‐ and human‐selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ‐at‐risk sparing. Conclusions This pilot study demonstrated the feasibility of a novel deep learning–based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human‐selected beam angles. In tandem with auto‐contouring and knowledge‐based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy.