Reconstructing and resizing 3D images from DICOM files

A reliable deep-learning-based method for alveolar bone quantification using a murine model of periodontitis and micro-computed tomography imaging

OBJECTIVES

This study focuses on artificial intelligence (AI)-assisted analysis of alveolar bone for periodontitis in a mouse model with the aim to create an automatic deep-learning segmentation model that enables researchers to easily examine alveolar bone from micro-computed tomography (µCT) data without needing prior machine learning knowledge.

METHODS

Ligature-induced experimental periodontitis was produced by placing a small-diameter silk sling ligature around the left maxillary second molar. At 4, 7, 9, or 14 days, the maxillary bone was harvested and processed with a µCT scanner (µCT-45, Scanco). Using Dragonfly (v2021.3), we developed a 3D deep learning model based on the U-Net AI deep learning engine for segmenting materials in complex images to measure alveolar bone volume (BV) and bone mineral density (BMD) while excluding the teeth from the measurements.

RESULTS

This model generates 3D segmentation output for a selected region of interest with over 98 % accuracy on different formats of µCT data. BV on the ligature side gradually decreased from 0.87 mm3 to 0.50 mm3 on day 9 and then increased to 0.63 mm3 on day 14. The ligature side lost 4.6 % of BMD on day 4, 9.6 % on day 7, 17.7 % on day 9, and 21.1 % on day 14.

CONCLUSIONS

This study developed an AI model that can be downloaded and easily applied, allowing researchers to assess metrics including BV, BMD, and trabecular bone thickness, while excluding teeth from the measurements of mouse alveolar bone.

CLINICAL SIGNIFICANCE

This work offers an innovative, user-friendly automatic segmentation model that is fast, accurate, and reliable, demonstrating new potential uses of artificial intelligence (AI) in dentistry with great potential in diagnosing, treating, and prognosis of oral diseases.

Development of biodegradable customized tibial scaffold with advanced architected materials utilizing additive manufacturing

In the last decade, the development of customized biodegradable scaffolds and implants has attracted increased scientific interest due to the fact that additive manufacturing technologies allow for the rapid production of implants with high geometric complexity constructed via commercial biodegradable polymers. In this study, innovative designs of tibial scaffold in form of bone-brick configuration were developed to fill the bone gap utilizing advanced architected materials and bio-inspired diffusion canals. The architected materials and canals provide high porosity, as well as a high surface area to volume ratio in the scaffold facilitating that way in the tissue regeneration process and in withstanding the applied external loads. The cellular structures applied in this work were the Schwarz Diamond (SD) and a hybrid SD&FCC hybrid cellular material, which is a completely new architected material that derived from the combination of SD and Face Centered Cubic (FCC) structures. These designs were additively manufactured utilizing two biodegradable materials namely Polylactic acid (PLA) and Polycaprolactone (PCL), using the Fused Filament Fabrication (FFF) technique, in order to avoid the surgery, for the scaffold's removal after the bone regeneration. Furthermore, the additively manufactured scaffolds were examined in terms of compatibility and assembly with the bone's physical model, as well as, in terms of mechanical behavior under realistic static loads. In addition, non-linear finite element models (FEMs) were developed based on the experimental data to accurately simulate the mechanical response of the examined scaffolds. The Finite Element Analysis (FEA) results were compared with the experimental response and afterwards the stress concentration regions were observed and identified. Τhe proposed design of scaffold with SD&FCC lattice structure made of PLA material with a relative density of 20% revealed the best overall performance, showing that it is the most suitable candidate for further investigation (in-vivo test, clinical trials, etc.) and commercialization.

Case report: A quantitative and qualitative diffusion tensor imaging (DTI) study in varicella zoster-related brachial plexopathy

Diffusion tensor imaging (DTI) is considered feasible for the nerve plexuses' imaging and quantitative evaluation but its value in the clinical practice is still virtually unexplored. We present the DTI profile of a case of acute varicella-zoster virus (VZV)-related brachial plexopathy. A 72-year-old woman presented with left upper-limb segmental paresis involving the spinal metamers C6-C7, preceded by a painful dermatomal vesicular eruption in C5-T1 dermatomes. Clinical and electrophysiological findings and magnetic resonance imaging indicated a plexus involvement. DTI analysis showed decreased fractional anisotropy (FA) and an increase of all the other diffusivity indexes, i.e., mean, axial, and radial diffusivity. The mechanisms underlying DTI parameter differences between healthy and pathologic brachial plexus sides could be related to microstructural fiber damage. Water diffusion is affected within the nerve roots by increasing the diffusion distance, leading to increased diffusion perpendicular to the largest eigenvalue and therefore to decreased FA values The role of DTI in clinical practice has not been defined yet. Additional quantitative and qualitative DTI information could improve the assessment and follow-up of brachial plexopathy.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.