The accuracy and reliability of different midsagittal planes in the symmetry assessment using cone-beam computed tomography.

Symmetry is an essential component of esthetic assessment. Accurate assessment of facial symmetry is critical to the treatment plan of orthognathic surgery and orthodontic treatment. However, there is no internationally accepted midsagittal plane (MSP) for orthodontists and orthognathic surgeons. The purpose of this study was to explore a clinically friendly MSP, which is more accurate and reliable than what is commonly used in symmetry assessment. Forty patients with symmetric craniofacial structures were analyzed on cone-beam computed tomography (CBCT) scans. The CBCT data were exported to the Simplant Pro software to build four reference planes that were constructed by nasion (N), basion (Ba), sella (S), odontoid (Dent), or incisive foramen (IF). A total of 31 landmarks were located to determine which reference plane is the most optimal MSP by comparing the asymmetry index (AI). The mean value of AI showed a significant difference (p < 0.05) among four reference planes. Also, the mean value of AI for all landmarks showed that Plane 2 (consisting of N, Ba, and IF) and Plane 4 (consisting of N, IF, and Dent) were more accurate and stable. In conclusion, the MSP consisting of N, Dent, and IF shows more accuracy and reliability than the other planes. Further, it is more clinically friendly because of its significant advantage in landmarking.

Phenotype Classification using Proteome Data in a Data-Independent Acquisition Tensor Format.

A novel approach for phenotype prediction is developed for data-independent acquisition (DIA) mass spectrometric (MS) data without the need for peptide precursor identification using existing DIA software tools. The first step converts the DIA-MS data file into a new file format called DIA tensor (DIAT), which can be used for the convenient visualization of all the ions from peptide precursors and fragments. DIAT files can be fed directly into a deep neural network to predict phenotypes such as appearances of cats, dogs, and microscopic images. As a proof of principle, we applied this approach to 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in distinguishing malignant from benign samples. We further applied a refined model to classify thyroid nodules. Deep learning based on 492 training samples achieved an accuracy of 91.7% in an independent cohort of 216 test samples. This approach surpassed the deep-learning model based on peptide and protein matrices generated by OpenSWATH. In summary, we present a new strategy for DIA data analysis based on a novel data format called DIAT, which enables facile two-dimensional visualization of DIA proteomics data. DIAT files can be directly used for deep learning for biological and clinical phenotype classification. Future research will interpret the deep-learning models emerged from DIAT analysis.