Radiomics and Artificial Intelligence Study of Masseter Muscle Segmentation in Patients With Hemifacial Microsomia.

BACKGROUND: Hemifacial microsomia (HFM) is one of the most common congenital craniofacial condition often accompanied by masseter muscle involvement. U-Net neural convolution network for masseter segmentation is expected to achieve an efficient evaluation of masseter muscle. METHODS: A database was established with 108 patients with HFM from June 2012 to June 2019 in our center. Demographic data, OMENS classification, and 1-mm layer thick 3-dimensional computed tomography were included. Two radiologists manually segmented masseter muscles in a consensus reading as the ground truth. A test set of 20 cases was duplicated into 2 groups: an experimental group with the intelligent algorithm and a control group with manual segmentation. The U-net follows the design of 3D RoI-Aware U-Net with overlapping window strategy and references to our previous study of masseter segmentation in a healthy population system. Sorensen dice-similarity coefficient (DSC) muscle volume, average surface distance, recall, and time were used to validate compared with the ground truth. RESULTS: The mean DSC value of 0.794±0.028 for the experiment group was compared with the manual segmentation (0.885±0.118) with α=0.05 and a noninferiority margin of 15%. In addition, higher DSC was reported in patients with milder mandible deformity ( r =0.824, P <0.05). Moreover, intelligent automatic segmentation takes only 6.4 seconds showing great efficiency. CONCLUSIONS: We first proposed a U-net neural convolutional network and achieved automatic segmentation of masseter muscles in patients with HFM. It is a great attempt at intelligent diagnosis and evaluation of craniofacial diseases.

Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images.

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

Radical Annulation of 2-Cyanoaryl Acrylamides via C═C Double Bond Cleavage: Access to Amino-Substituted 2-Quinolones.

A novel annulation of 2-cyanoaryl acrylamides via C═C double bond cleavage has been developed for facile and efficient access to a broad spectrum of functionalized 4-amino-2-quinolones, which are important N-heterocycles. In this transformation, the solvent THF is demonstrated to play a crucial role, and the addition of alkyl radicals to nitrile, 1,5-hydride shift, and cleavage of the C-C bond are involved in the mechanism.

Decarboxylative Oxyacyloxylation of Propiolic Acids: Construction of Alkynyl-Containing α-Acyloxy Ketones.

Novel decarboxylative oxyacyloxylation of propiolic acids has been developed. This reaction provides an efficient access to alkynyl-containing α-acyloxy ketones from readily available starting materials and exhibits significant functional group tolerance. Furthermore, oxyacyloxylation of terminal alkynes and aliphatic propiolic acids was also developed. A possible reaction mechanism is proposed based on mechanistic studies.

Using machine learning to identify clotted specimens in coagulation testing.

OBJECTIVES: A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens. METHODS: The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset. RESULTS: Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958-0.974) and 0.971 (95% CI, 0.9641-0.9784), respectively. CONCLUSIONS: Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.

Copper-Catalyzed Decarboxylative Cycloaddition of Propiolic Acids, Azides, and Arylboronic Acids: Construction of Fully Substituted 1,2,3-Triazoles.

A copper-catalyzed decarboxylative cycloaddition of propiolic acids, azides, and arylboronic acids is described. The present reaction provides an efficient and convenient method for the synthesis of various fully substituted 1,2,3-triazoles from readily available starting materials. A possible mechanism is proposed.

Copper-Catalyzed Decarboxylative Oxyalkylation of Alkynyl Carboxylic Acids: Synthesis of γ-Diketones and γ-Ketonitriles.

A novel copper-catalyzed decarboxylative oxyalkylation of alkynyl carboxylic acids with ketones and alkylnitriles via direct C(sp3)-H bond functionalization to construct new C-C bonds and C-O double bonds was developed. This transformation is featured by wide functional group compatibility and the use of readily available reagents, thus affording a general approach to γ-diketones and γ-ketonitriles. A possible mechanism is proposed.

Automatic Masseter Thickness Measurement and Ideal Point Localization for Botulinum Toxin Injection.

Botulinum toxin injection is a highly efficacious treatment for masseter muscle hypertrophy, while manual injection point localization based on experience can be non-quantitative, subjective and therefore suboptimal and even side-effect risky. To address this important while challenging task, in this paper we present a methodology of automatic ideal point localization for botulinum toxin injection based on automatic segmentation, measurement and quantitative analysis. Specifically, we first present a novel three-dimensional (3D) fully convolutional neural network for fully automatic mandible and masseter regions of interest (RoI) localization and segmentation from head computed tomography (CT) images. Given the segmentation results, the ideal injection points on the face are located using ray casting based automatic thickness measurement. We conducted experiments on an internal dataset consisting of head CT images acquired from 53 patients to evaluate the segmentation performance and localization reliability. The results demonstrate that the segmentation framework outperforms the state-of-the-art method by a significant margin, and the localization system provides intuitive, interactive user interface and reliable injection point decisions.

Metal-Free Radical [2 + 2 + 1] Cyclization of ortho-Cyanoarylacrylamides with Alkyl Nitriles: Synthesis of Pyrrolo[3,2- c]quinolines.

A novel radical [2 + 2 + 1] cyclization of ortho-cyanoarylacrylamides with dual α-C-H bonds in alkyl nitriles has been developed. The reaction provides new facile and straightforward access to cyano-substituted pyrrolo[3,2- c]quinolines, which are important nitrogen-containing polyheterocycles. A possible mechanism for the transformation is proposed.