Learning soft tissue deformation from incremental simulations.

Background: Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM).

Alliin Induces Reconstitution of Testes Damaged by Estrogen Overstimulation by Regulating Apoptosis.

We analyzed the effect of alliin on the recovery of mouse testicular function and structure following estradiol treatment as well as on apoptosis regulation. During the cultivation of testicular cells, high-concentration estradiol suppressed Casp-3; PCNA, mTOR, and PI3K signaling increased; and cell proliferation in the testes was abnormally increased. Therefore, estradiol treatment increased the proportion of abnormal cells.

Large Language Models Diagnose Facial Deformity.

Purpose: This study examines the application of Large Language Models (LLMs) in diagnosing jaw deformities, aiming to overcome the limitations of various diagnostic methods by harnessing the advanced capabilities of LLMs for enhanced data interpretation. The goal is to provide tools that simplify complex data analysis and make diagnostic processes more accessible and intuitive for clinical practitioners.

Methods: An experiment involving patients with jaw deformities was conducted, where cephalometric measurements (SNB Angle, Facial Angle, Mandibular Unit Length) were converted into text for LLM analysis.

Correspondence attention for facial appearance simulation.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM.

Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities.

Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial aesthetics and residual asymmetry. To address these issues, a soft-tissue-driven planning method has been proposed.

Machine Learning Effectively Diagnoses Mandibular Deformity Using Three-Dimensional Landmarks.

Background: Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions.

Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.

This paper proposes a deep learning framework to encode subject-specific transformations between facial and bony shapes for orthognathic surgical planning. Our framework involves a bidirectional point-to-point convolutional network (P2P-Conv) to predict the transformations between facial and bony shapes. P2P-Conv is an extension of the state-of-the-art P2P-Net and leverages dynamic point-wise convolution (i.

Identification and Characterization of Cancer-Associated Fibroblast Subpopulations in Lung Adenocarcinoma.

Cancer-associated fibroblasts (CAFs) reside within the tumor microenvironment, facilitating cancer progression and metastasis via direct and indirect interactions with cancer cells and other stromal cell types. CAFs are composed of heterogeneous subpopulations of activated fibroblasts, including myofibroblastic, inflammatory, and immunosuppressive CAFs. In this study, we sought to identify subpopulations of CAFs isolated from human lung adenocarcinomas and describe their transcriptomic and functional characteristics through single-cell RNA sequencing (scRNA-seq) and subsequent bioinformatics analyses.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.

Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive.

Phase retrieval based on deep learning in grating interferometer.

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts.

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

Purpose: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed.

Analysis of a silicon comb structure using an inverse Talbot-Lau neutron grating interferometer.

We describe an inverse Talbot-Lau neutron grating interferometer that provides an extended autocorrelation length range for quantitative dark-field imaging. To our knowledge, this is the first report of a Talbot-Lau neutron grating interferometer (nTLI) with inverse geometry. We demonstrate a range of autocorrelation lengths (ACL) starting at low tens of nanometers, which is significantly extended compared to the ranges of conventional and symmetric setups.

Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning.

Facial appearance changes with the movements of bony segments in orthognathic surgery of patients with craniomaxillofacial (CMF) deformities. Conventional bio-mechanical methods, such as finite element modeling (FEM), for simulating such changes, are labor intensive and computationally expensive, preventing them from being used in clinical settings. To overcome these limitations, we propose a deep learning framework to predict post-operative facial changes.

Midsagittal Plane First: Building a Strong Facial Reference Frame for Computer-Aided Surgical Simulation.

Purpose: A facial reference frame is a 3-dimensional Cartesian coordinate system that includes 3 perpendicular planes: midsagittal, axial, and coronal. The order in which one defines the planes matters. The purposes of this study are to determine the following: 1) what sequence (axial-midsagittal-coronal vs midsagittal-axial-coronal) produced more appropriate reference frames and 2) whether orbital or auricular dystopia influenced the outcomes.

A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning.

Virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. Due to the lack of necessary guidance, the planning procedure is highly experience-dependent and the planning results are often suboptimal. A reference facial bony shape model representing normal anatomies can provide an objective guidance to improve planning accuracy.

Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network.

Purpose: The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities.

Methods: We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient-specific normal bony shapes.

A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect.

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points.

Study on dark-field imaging with a laboratory x-ray source: Random stress variation analysis based on x-ray grating interferometry.

The dark-field image (DFI) in a grating interferometer involves the small-angle scattering properties of a material. The microstructure of the material can be characterized by an analysis of the auto-correlation length and the DFI. The feasibility of a DFI in a laboratory x-ray source with grating interferometry has been reported, but a follow-up study is needed.

Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning.

Orthognathic surgical outcomes rely heavily on the quality of surgical planning. Automatic estimation of a reference facial bone shape significantly reduces experience-dependent variability and improves planning accuracy and efficiency. We propose an end-to-end deep learning framework to estimate patient-specific reference bony shape models for patients with orthognathic deformities.

Efficacy and safety of transvaginal high-intensity focused ultrasound therapy in women with symptomatic uterine leiomyomas: A clinical trial.

Objective: To evaluate the efficacy and safety of transvaginal high-intensity focused ultrasound (vHIFU) therapy in women with symptomatic uterine leiomyomas.

Methods: This first-in-human, two-center, prospective, unblinded, single-arm trial was performed in the Republic of Korea from December 2017 to February 2019. Premenopausal women with symptomatic, contrast-enhanced uterine leiomyomas with a diameter ≤5 cm were eligible.

Neutron grating interferometer with an analyzer grating based on a light blocker.

We study an analyzer grating based on a scintillation light blocker for a Talbot-Lau grating interferometer. This is an alternative way to analyze the Talbot self-image without the need for an often difficult to fabricate absorption grating for the incident radiation. The feasibility of this approach using a neutron beam has been evaluated and experiments have been conducted at the cold neutron imaging facility of the NIST center for Neutron Research.

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging.

In Talbot-Lau interferometry, the sample position yielding the highest phase sensitivity suffers from strong geometric blur. This trade-off between phase-sensitivity and spatial resolution is a fundamental challenge in such interferometric imaging applications with either neutron or conventional x-ray sources due to their relatively large beam-defining apertures or focal spots. In this study, a deep learning method is introduced to estimate a high phase-sensitive and high spatial resolution image from a trained neural network to attempt to avoid the trade-off for both high phase-sensitivity and high resolution.

An automatic approach to establish clinically desired final dental occlusion for one-piece maxillary orthognathic surgery.

Purpose: One critical step in routine orthognathic surgery is to reestablish a desired final dental occlusion. Traditionally, the final occlusion is established by hand articulating stone dental models. To date, there are still no effective solutions to establish the final occlusion in computer-aided surgical simulation.

Clinical Evaluation of Digital Dental Articulation for One-Piece Maxillary Surgery.

Purpose: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion.

Materials And Methods: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used.