Digital Imaging And Communications In Medicine Files Research Articles

Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks

ObjectiveThis study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images.BackgroundIn orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate.MethodsSamples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models.ResultsThe supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high.ConclusionIn this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks.

Comparing the Output of an Artificial Intelligence Algorithm in Detecting Radiological Signs of Pulmonary Tuberculosis in Digital Chest X-Rays and Their Smartphone-Captured Photos of X-Ray Films: Retrospective Study.

Artificial intelligence (AI) based computer-aided detection devices are recommended for screening and triaging of pulmonary tuberculosis (TB) using digital chest x-ray (CXR) images (soft copies). Most AI algorithms are trained using input data from digital CXR Digital Imaging and Communications in Medicine (DICOM) files. There can be scenarios when only digital CXR films (hard copies) are available for interpretation. A smartphone-captured photo of the digital CXR film may be used for AI to process in such a scenario. There is a gap in the literature investigating if there is a significant difference in the performance of AI algorithms when digital CXR DICOM files are used as input for AI to process as opposed to photos of the digital CXR films being used as input. The primary objective was to compare the agreement of AI in detecting radiological signs of TB when using DICOM files (denoted as CXRd) as input versus when using smartphone-captured photos of digital CXR films (denoted as CXRp) with human readers. Pairs of CXRd and CXRp images were obtained retrospectively from patients screened for TB. AI results were obtained using both the CXRd and CXRp files. The majority consensus on the presence or absence of TB in CXR pairs was obtained from a panel of 3 independent radiologists. The positive and negative percent agreement of AI in detecting radiological signs of TB in CXRd and CXRp were estimated by comparing with the majority consensus. The distribution of AI probability scores was also compared. A total of 1278 CXR pairs were analyzed. The positive percent agreement of AI was found to be 92.22% (95% CI 89.94-94.12) and 90.75% (95% CI 88.32-92.82), respectively, for CXRd and CXRp images (P=.09). The negative percent agreement of AI was 82.08% (95% CI 78.76-85.07) and 79.23% (95% CI 75.75-82.42), respectively, for CXRd and CXRp images (P=.06). The median of the AI probability score was 0.72 (IQR 0.11-0.97) in CXRd and 0.72 (IQR 0.14-0.96) in CXRp images (P=.75). We did not observe any statistically significant differences in the output of AI in digital CXRs and photos of digital CXR films.

The Precision of All-on-Four Implant Position Recorded from Three Different CBCT Machines.

To investigate the dimensional discrepancy and degree of deviation of All-on-Four implant position between different cone-beam computed tomography (CBCT) machines. Four implants (4.5 × 10 mm Superline II, Dentium, South Korea) were placed in an All-on-Four style in an artificial mandible. The jaw was radiated 30 times using three different CBCT machines (Rainbow CT, Dentium; Veraview X800, Morita, Japan; Planmeca Viso G3, Planmeca OY, Finland). A total of 30 Digital Imaging and Communications in Medicine (DICOM) files were exported, n = 10. All-on-Four implants from each DICOM file were segmented and exported as an STL file (three-dimensional image) using Blue Sky Plan software (version 4.12.13/Blue Sky Bio, United States). All-on-Four implant zone dimensions (X, Y, and Z axes) and the total degree of deviation between All-on-Four implants per CBCT machine were measured using Autodesk Meshmixer software (version 3.5.474/California, United States). The data distribution's normality and variances' equality were tested with Shapiro-Wilk's and Levene's tests, respectively (p-value < 0.05). Data were analyzed using Brown-Forsythe one-way analysis of variance and Tamhane's post hoc tests to compare the differences between the groups (p-value <0.05). The respective X, Y, and Z mean dimensions of the All-on-Four implant zone were: Dentium (34.95, 14.71, and 9.97); Morita (34.88, 14.74, and 10.56); and Planmeca (34.73, 15.15, and 12.33). Significant differences between CBCT machines were found in all axes (p-value < 0.05); however, the Z-axis had the most differences. Notably, Planmeca exhibited the highest standard deviation (SD) in all axes (0.16-0.35), exhibiting the lowest consistency in the CBCT machines' readings. The Dentium exhibited the lowest deviation in the implant position, with the lowest SD (0.61). A significant difference in the total degree of deviation was spotted when only Morita was included in the comparison (p-value < 0.05). This study's findings are of significant importance as they reveal that the implant position recorded from the CBCT machines was most discrepant in the buccolingual dimension (Z-axis). Planmeca exhibited the least implant-dimensional accuracy of the CBCT machines, while Dentium exhibited the highest implant position accuracy. These results could significantly impact the choice of CBCT machine for implant placement, especially since an accurate CBCT image is crucial for digital implant planning.

Pre-Operative Planning of a DCR Surgery Using Virtual Reality

ABSTRACT Purpose To assess a novel Virtual Reality (VR) tool designed to enhance understanding of the nasal anatomy in patients eligible for DCR surgery. Methods Preoperative Computed Tomography (CT) scans of the orbit were obtained and loaded as DICOM (Digital Imaging and Communications in Medicine) files onto the D2P software (3D Systems Inc. Littleton, CO) for tissue segmentation and 3D model preparation. Segmentation was performed on several anatomical structures, including the skull, lacrimal sac, nasal septum, inferior and middle turbinate. The resulting 3D model was visualized using a VR headset. After completing the segmentation procedure, ten cases were evaluated by a panel of six surgeons, including both senior and resident physicians from ENT and oculoplastic specialties. Results The dataset under examination comprised images from 10 preoperative CT scans of the orbits of patients eligible for Endo-DCR. When evaluating the CT using the VR tool, in 73.3% of the cases ENT surgeons were right about the side of pathology, while only 43.3% ophthalmologists were right (chi-square, p = .018). In 72.8% of the cases ENT surgeons were evaluated right that there is a septum deviation, while only in 47.2% of the cases the ophthalmologists were right (chi-square, p = .094). When evaluating the CT using the VR tool, in 60% of the cases consultants were right about the pathology, while 57.7% of the residents were right (chi-square, p = .853). In 81.7% of the cases consultants were evaluated right that there is a septum deviation, while only in 58.3% of the cases the ophthalmologists were right (chi-square, p = .198). Discussion ENT surgeons, as well as consultants, interpreted the CT better than the ophthalmologists and residents. Surprisingly, the VR system did not help them to interpret the CT better. Further, more extensive studies should be done to build a VR system that assists in the correct interpretation of the preoperative CT before DCR surgery as well as during DCR surgery.

A Cost-Affordable Methodology of 3D Printing of Bone Fractures Using DICOM Files in Traumatology

Three-dimensional (3D) printing has gained popularity across various domains but remains less integrated into medical surgery due to its complexity. Existing literature primarily discusses specific applications, with limited detailed guidance on the entire process. The methodological details of converting Computed Tomography (CT) images into 3D models are often found in amateur 3D printing forums rather than scientific literature. To address this gap, we present a comprehensive methodology for converting CT images of bone fractures into 3D-printed models. This involves transferring files in Digital Imaging and Communications in Medicine (DICOM) format to stereolithography format, processing the 3D model, and preparing it for printing. Our methodology outlines step-by-step guidelines, time estimates, and software recommendations, prioritizing free open-source tools. We also share our practical experience and outcomes, including the successful creation of 72 models for surgical planning, patient education, and teaching. Although there are challenges associated with utilizing 3D printing in surgery, such as the requirement for specialized expertise and equipment, the advantages in surgical planning, patient education, and improved outcomes are evident. Further studies are warranted to refine and standardize these methodologies for broader adoption in medical practice.

Study on the characteristics of increased mechanical stiffness according to changes in LCP shape to reinforce clavicle fractures

The clavicle has various anatomic shapes unique to each individual. Additionally, with the increase in high-energy traumas such as sports injuries and traffic accidents, the patterns of fractures become complex and complicated. Thus, there is a need for a variety of shapes of locking compression plates (LCP) to accommodate different types of fractures and facilitate quicker rehabilitation. The aim of this study is to present different types of LCP that secure fracture fragments and distribute stress evenly, in comparison to typical anatomical LCPs, for reinforcing clavicle fractures. Three models were compared in this study: the typical shape, the center hole removed shape, and the double-curved wing shape. The DICOM (Digital Imaging and Communications in Medicine) file obtained from the computed tomography scan of the patient’s clavicle was used to extract the three-dimensional (3D) clavicle structure. Finite element analysis (FEA) simulation was employed to analyze the structural changes of the LCP under external forces. A reinforced jig was used to apply the same type of external force to each LCP, and an experiment was conducted to analyze the mechanical impact of the LCP’s structural characteristics. When comparing the stress values at the fracture zone point, resulting from the FEA simulation with applied bending forces, it was calculated that the stress dispersion effect was approximately ten times greater when transitioning from a typical LCP shape to a double-curved partial wing structure. Moreover, the ultimate stress increased 3.33 times, from 241.322 to 804.057 N, as the LCP design changed under cantilever bending conditions. This double-curved wing LCP design reduces stress concentration at the fracture site and minimizes stress in the fracture area when subjected to cantilever bending forces. Consequently, this newly designed LCP has the potential to decrease complications related to the plate and accelerate rehabilitation protocols.

Feasibility of cross-vendor linkage of ophthalmic images with electronic health record data: an analysis from the IRIS Registry®.

To link compliant, universal Digital Imaging and Communications in Medicine (DICOM) ophthalmic imaging data at the individual patient level with the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight). A retrospective study using de-identified EHR registry data. IRIS Registry records. DICOM files of several imaging modalities were acquired from two large retina ophthalmology practices. Metadata tags were extracted and harmonized to facilitate linkage to the IRIS Registry using a proprietary, heuristic patient-matching algorithm, adhering to HITRUST guidelines. Linked patients and images were assessed by image type and clinical diagnosis. Reasons for failed linkage were assessed by examining patients' records. Success rate of linking clinicoimaging and EHR data at the patient level. A total of 2 287 839 DICOM files from 54 896 unique patients were available. Of these, 1 937 864 images from 46 196 unique patients were successfully linked to existing patients in the registry. After removing records with abnormal patient names and invalid birthdates, the success linkage rate was 93.3% for images. 88.2% of all patients at the participating practices were linked to at least one image. Using identifiers from DICOM metadata, we created an automated pipeline to connect longitudinal real-world clinical data comprehensively and accurately to various imaging modalities from multiple manufacturers at the patient and visit levels. The process has produced an enriched and multimodal IRIS Registry, bridging the gap between basic research and clinical care by enabling future applications in artificial intelligence algorithmic development requiring large linked clinicoimaging datasets.

Bone Volume Assessment Around Immediate Dental Implant in the Esthetic Area with Immediate Tooth-Like Single Crown Provisionalization: A Laser and Computed Tomography 1-Year Retrospective Study.

To examine the remodeling process of both the soft and hard tissue components of the postextraction socket around immediately loaded dental implants after tooth extraction in maxillary esthetic areas. Subjects underwent immediate placement of single implants in postextraction sockets without bone grafting, and their immediate provisionalization with custom tooth-like interim crowns were fabricated using digital diagnostic impressions and a dental milling machine. Intraoperative and 1-year follow-up layered scans of the postextraction sockets after implantation were acquired using a 3D optical system. In the short term, subjects underwent computed tomographic scans. Digital impressions for gingival contours, originally stored as STL (standard tessellation language) files, were converted to DICOM (Digital Imaging and Communications in Medicine) files with the implant shoulder working as a referral point, which were then superimposed to 3D radiologic images. The observed volumetric and linear outcomes were measured using a program known as DentaScan. The width of the alveolar crest at the level of the implant shoulder and marginal bone levels were acquired. Nonparametric tests were applied with a level of significance set at P < .01. No failure was reported after a follow-up of 1 year. Little or no inflammation of the treated areas was registered, and there were practically no signs of suppuration. The areas showed a significant reduction in the overall volumes for both soft and bone tissue, with a P value < .0001 from the baseline (0.983 ± 0.172 cm3) to the 1-year survey (0.865 ± 0.156 cm3). If the soft and bone tissue changes were separately evaluated, a significant loss (with a P value < .0001) was registered for only the bone tissues (from 0.434 ± 0.075 to 0.355 ± 0.061 cm3). Moreover, changes in gingival tissue from baseline to the 1-year survey (-0.040 ± 0.067) appeared to be significantly different from the overall volume loss (-0.118 ± 0.083 cm3). A shrinkage in width (-0.5 ± 0.7 mm) was found from baseline (12.6 ± 0.6 mm) to the 1-year follow-up (12.1 ± 0.9 mm). Marginal bone levels were 0.97 ± 0.70 mm and 0.39 ± 0.78 mm, respectively, at the mesial and distal aspects of the implants. The present analysis suggested that immediately customized provisionalization was effective enough to prevent both volume loss and linear shrinkage at the layers of the treated areas. Moreover, the buccal aspects seemed to be the areas most affected by the loss of volume. The mean loss in width, which amounted to roughly 0.5 mm, appeared to be negligible when compared to the overall width measured before surgery.

Non-invasive assessment of peri-implant mucosal thickness: A cross-sectional study.

This study aimed to evaluate the reliability and reproducibility of different non-invasive methods for the assessment of peri-implant mucosal thickness. Subjects with two adjacent dental implants in the central maxillary region were included in this study. Three different methods to assess facial mucosal thickness (FMT) were compared: digital file superimposition using Digital Imaging and Communication in Medicine (DICOM) and stereolithography (STL) files of the arch of interest (DICOM-STL), DICOM files alone, and non-ionizing ultrasound (US). Inter-rater reliability agreements between different assessment methods were analyzed using inter-class correlation coefficients (ICCs). A total of 50 subjects with 100 bone-level implants constituted the study population. Assessment of FMT using STL and DICOM files demonstrated excellent inter-rater reliability agreement. Mean ICC values of 0.97 and 0.95 were observed in the DICOM-STL and DICOM groups, respectively. Comparison between the DICOM-STL and US revealed good agreement, with an ICC of 0.82 (95% CI: 0.74 to 0.88) and a mean difference of -0.13±0.50mm (-1.13 to 0.86). Comparison between DICOM files alone versus US showed good agreement, with an ICC of 0.81 (95% CI: 0.73 to 0.89) and a mean difference of -0.23±0.46mm (-1.12 to 0.67). Comparison between DICOM-STL and DICOM files revealed excellent agreement, with an ICC of 0.94 (95% CI: 0.91 to 0.96) and a mean difference of 0.1±0.29mm (LOA -0.47 to 0.46). Quantification of peri-implant mucosal thickness via analysis of DICOM-STL files, DICOM files, or US assessment are comparably reliable and reproducible methods.

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

Deep Learning Dramatically Reduces the Work Associated with Image Cataloguing and Analysis: Commentary on an article by Pouria Rouzrokh, MD, MPH, MHPE, et al.: "Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry. A Stepwise Approach".

Commentary Artificial intelligence (AI), machine learning, neural networks, and shallow and deep learning are all forms of data analytics that are creeping into the orthopaedic lexicon1—with good reason, as these forms of data analytics have also become prevalent across medicine and in many aspects of life outside of medicine and health care. It is imperative that the field of orthopaedics take advantage of tools that have demonstrated benefit in other related applications in order to advance orthopaedic research and ultimately point-of-care decisions. The article by Rouzrokh et al. describes and evaluates a process employing a form of AI known as deep learning to identify and catalogue hip images from an uncatalogued institutional image warehouse. In essence, the study seeks to determine whether a computer can do what has previously been the tedious manual work of clinicians and investigators seeking to identify and measure images for research purposes. There are a number of aspects to this manuscript that make it an excellent read. First of all, the authors provide readers with tangible evidence that deep learning can be applied with confidence to identify images of certain descriptions for registry or quality-improvement purposes. Next, reading this manuscript will help orthopaedic surgeons to understand the language and meaning of deep learning. Additionally, this paper demonstrates how a deep-learning process can be utilized to both identify and perform simple measurements on a large collection of images from a data warehouse. Consider that validation of the AI process, which was performed as part of the methods of this analysis, necessitated human review of 5,000 images. The actual automated AI analysis took less than 8 hours to perform. Although the authors do not state how much time the human review of images required, one can safely assume that it was far greater than the 8 hours required by the algorithm to identify and measure 846,988 DICOM (digital imaging and communications in medicine) files. Thus, the article demonstrates the massive reduction in work that this technology offers. The authors offer the possibility that the 3-step pipeline to create their tool could be utilized “by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas.” These comments and other reports in the literature clearly suggest that the technology applied to this total hip arthroplasty radiographic registry is possibly applicable to the work of investigators in other subspecialties2. Likewise, there is a community of investigators utilizing deep-learning tools who are prepared to share their experience and expand the use of these tools more broadly in our profession. Finally, as one looks ahead at the future and what will grow from the introduction of this technology into musculoskeletal science, it is clear that we are only at the beginning of what is possible. The authors have done an excellent job of demonstrating how this technology may be utilized to perform the simple tasks of image identification and angular measurement with an acceptable degree of accuracy. One can only imagine that more sophisticated image identification and measurement are possible by developing similar systems with greater maturity, sophistication, and experience. Deep learning could be utilized to provide other types of data that would be useful both in clinical investigation and at the point of care, such as markers of implant loosening, wear, or osteolysis, or measurement of skeletal morphology from more sophisticated and advanced imaging3. There are endless possibilities for what might be made possible by such robust analytics in our new digital and connected world.

Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry: A Stepwise Approach.

Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images. We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs. Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance. We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas. Diagnostic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Three-dimensionally rendering of the sphenoid bone of adolescents using Materialise’s Interactive Medical Image Control System software

The main goal of this study was to reconstruct three-dimensionally (3D) the sphenoid bone of adolescents with the software Materialise Mimics to test the accuracy and reliability of craniometric measurements performed with the software. The study was conducted according to Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines. Cone-Beam Computed Tomography (CBCT) was performed in adolescents before the orthodontic treatment as part of the orthodontic records. The CBCT images were exported as DICOM (Digital Imaging and Communication in Medicine) files, in a universal format, with a voxel size of 0.3 mm and sphenoid bone was three-dimensionally rendered with Software Materialise Mimics. Ten sphenoid measurements were performed in triplicate by two trained examiners. The studied population was composed of 26 adolescents, 16 females (61.5%), and 10 males (38.5%) with a mean age of 12.5 years (SD= 1.7). 60 measurements were taken and the intra and inter-examiner accuracy revealed a high degree of data reproducibility (Kappa test higher than 0.90). The reconstruction and rendering of the images obtained by CBTC allowed anatomical details of the sphenoid bone to be measured with very high reproducibility. The Software Materialise Mimics allows you to analyze anatomical structures in detail and presents useful tools to optimize craniometry analyses.

An Encryption Algorithm for Region of Interest in Medical DICOM Based on One-Dimensional eλ-cos-cot Map.

Today, with the rapid development of the Internet, improving image security becomes more and more important. To improve image encryption efficiency, a novel region of interest (ROI) encryption algorithm based on a chaotic system was proposed. First, a new 1D -cos-cot (1D-ECC) with better chaotic performance than the traditional chaotic system is proposed. Second, the chaotic system is used to generate a plaintext-relate keystream based on the label information of a medical image DICOM (Digital Imaging and Communications in Medicine) file, the medical image is segmented using an adaptive threshold, and the segmented region of interest is encrypted. The encryption process is divided into two stages: scrambling and diffusion. In the scrambling stage, helical scanning and index scrambling are combined to scramble. In the diffusion stage, two-dimensional bi-directional diffusion is adopted, that is, the image is bi-directionally diffused row by column to make image security better. The algorithm offers good encryption speed and security performance, according to simulation results and security analysis.

Evaluation of the accuracy of soft tissue thickness measurements with three different methodologies: An in vitro study.

Soft tissue thickness (STT) influences esthetics, peri-implant, and periodontal health. Non-invasive methods of STT evaluation include cone-beam computed tomography (CBCT) with Digital Imaging and Communications in Medicine (DICOM) files and registration of DICOM files with an intraoral scan or Standard Tessellation Language (STL) files. This study compares three methodologies: bone sounding, DICOM data alone, and DICOM and STL registration to absolute histomorphologic values. Five human maxillas, including teeth numbers 6 to 11, provided 90 sites for analysis. For standardization, reference grooves were placed at the cervical margin and the long axis of each tooth. Direct measurements with a no. 25 K-file were completed at the facial soft tissues at 3.00, 5.00, and 7.00mm from the apical marginal reference. Indirect measures were performed with implant planning software. Histological measurements were rendered with imaging software. One-way analysis of variance (ANOVA) was used to compare the three techniques for the differences from histologic measurements (α=0.05). Seventy-two sites were included for final analysis. The overall mean histological STT (mSTT) was 0.73±0.31mm. Bone sounding overestimated mSTT, 0.22±0.20mm (P&lt;0.001); whereas, DICOM alone underestimated mSTT, -0.23±0.19mm (P&lt;0.001). DICOM and STL registration had non-statistically significant differences, -0.04±0.21mm (P=0.429). Intraclass correlation coefficient (ICC) of DICOM and STL registration achieved the highest agreement with histology (ICC: 0.74). DICOM and STL file registration had the highest agreement with histological STT supporting the use of DICOM and STL registration for the evaluation of STT.

Information Hiding in the DICOM Message Service and Upper Layer Service with Entropy-Based Detection.

The DICOM (Digital Imaging and COmmunication in Medicine) standard provides a framework for a diagnostically-accurate representation, processing, transfer, storage and display of medical imaging data. Information hiding in DICOM is currently limited to the application of digital media steganography and watermarking techniques on the media parts of DICOM files, as well as text steganographic techniques for embedding information in metadata of DICOM files. To improve the overall security of the DICOM standard, we investigate its susceptibility to network steganographic techniques. To this aim, we develop several network covert channels that can be created by using a specific transport mechanism – the DICOM Message Service and Upper Layer Service. The bandwidth, undetectability and robustness of the proposed covert channels are evaluated, and potential countermeasures are suggested. Moreover, a detection mechanism leveraging entropy-based metrics is introduced and its performance has been assessed.

Development and implementation of a three-dimensional (3D) printing elective course for health science students.

Three-dimensional (3D) printing technology has become more affordable, accessible, and relevant in healthcare, however, the knowledge of transforming medical images to physical prints still requires some level of training. Anatomy educators can play a pivotal role in introducing learners to 3D printing due to the spatial context inherent to learning anatomy. To bridge this knowledge gap and decrease the intimidation associated with learning 3D printing technology, an elective was developed through a collaboration between the Department of Anatomy and the Makers Lab at the University of California, San Francisco. A self-directed digital resource was created for the elective to guide learners through the 3D printing workflow, which begins with a patient's computed tomography digital imaging and communication in medicine (DICOM) file to a physical 3D printed model. In addition to practicing the 3D printing workflow during the elective, a series of guest speakers presented on 3D printing applications they utilize in their clinical practice and/or research laboratories. Student evaluations indicated that their intimidation associated with 3D printing decreased, the clinical and research topics were directly applicable to their intended careers, and they enjoyed the autonomy associated with the elective format. The elective and the associated digital resource provided students with the foundational knowledge of 3D printing, including the ability to extract, edit, manipulate, and 3D print from DICOM files, making 3D printing more accessible. The aim of disseminating this work is to help other anatomy educators adopt this curriculum at their institution.

Design of virtual reality for teaching ultrasound in medical and engineering education

This works presents the design of a virtual reality (VR) platform that generates 3D models from ultrasound imaging used to teach medical and engineering students about anatomical structures. The VR platform will allow students to learn about the theory of wave propagation in tissues by providing 3D visualizations of abstract physics concepts and organ systems in an immersive and engaging environment. This phase of the project has been focused on learning to develop VR environments and how to translate medical imaging formats into a feasible tool. The specific focus has been on converting DICOM (Digital Imaging and Communications in Medicine) files into elements that can be managed in the VR environment (Unity 3D Development). This includes converting imaging data sets into 3D meshes that can be displayed and manipulated. The nearly infinite control over virtual environments can present alternative visualizations of ultrasound that may be accessible to the learner. The use of acquired ultrasound data within the virtual space will help learners transfer skills into point-of-care ultrasound (POCUS). The VR environment will help students to explore ultrasound without the limitations of a real patient and guide them to the physics principles to better understand ultrasound propagation in the human body.

3D printed patient-specific bone models for anatomy education from medical imaging

Evolution of 3D printing from medical image datasets are escalating and has widespread in healthcare applications such as anatomical models, surgical guides, and customized implants. In 3D printing, solid objects are fabricated by the frequently added the thin layers of material as per the digital model. This paper demonstrates the fabrication of 3D printed patient-specific bone models of leg and ankle foot from Digital Imaging and Communications in Medicine (DICOM) files. Processing of DICOM file is prepared by D2P (DICOM to PRINT) software and physical models are produced by Stratasys uPrint 3D printer. This 3D printed anatomical model eliminates the requirement of actual human bones, significance of preservation and mistakes in assembly of bones. The results of the study not only encourage education, surgical planning and validating medical devices but stimulate exciting innovations.

A Mandible with the Temporomandibular Joint-A New FEM Model Dedicated to Strength and Fatigue Calculations of Bonding Elements Used in Fracture and Defect Surgery.

The aim of the study was to develop a new FEM (finite element method) model of a mandible with the temporal joint, which can be used in the numerical verification of the work of bonding elements used in surgical operations of patients with mandibular fractures or defects. Most of such types of numerical models are dedicated to a specific case. The authors engaged themselves in building a model that can be relatively easily adapted to various types of tasks, allowing to assess stiffness, strength and durability of the bonded fragments, taking into account operational loads and fatigue limit that vary in time. The source of data constituting the basis for the construction of the model were DICOM (digital imaging and communications in medicine) files from medical imaging using computed tomography. On their basis, using the 3D Slicer program and algorithms based on the Hounsfield scale, a 3D model was created in the STL (standard triangle language) format. A CAD (computer-aided design) model was created using VRMesh and SolidWorks. An FEM model was built using HyperWorks and Abaqus/CAE. Abaqus solver was used for FEM analyses. A model meeting the adopted assumptions was built. The verification was conducted by analyzing the influence of the simplifications of the temporomandibular joint in the assessment of mandibular strain. The work of an undamaged mandible and the work of the bonded fracture of the mandible were simulated.