External surface anatomy of the postfolding human embryo: Computer-aided, three-dimensional reconstruction of printable digital specimens.
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ABSTRACT:
SUBMITTER: Azkue JJ
PROVIDER: S-EPMC8602026 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = - 0.01 (10), mean difference = - 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68-0.87, sensitivity of 1.00, precision of 0.50-0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.
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Project description:BackgroundMinimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery.MethodsThis systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time.ResultsAfter full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation.ConclusionsThe accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research.Trial registrationPROSPERO registration number: CRD42021264226.
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Project description:IntroductionComputer-aided design and computer-aided manufacturing (CAD/CAM) technology is an innovative digital system capable of scanning prepared teeth that are intended to receive crowns, bridges, and inlays and then effectively designing and fabricating restorations. Many dental schools are currently implementing this innovative CAD/CAM technology as part of their curricula and at University of Florida College of Dentistry we created a hands-on elective.MethodsThe 5-week course requires 2- to 3-hours of time per week for the lectures and labs. The sessions cover an introduction to digital dentistry, technology and systems evaluation, an exercise for scanning, designing, milling, and finishing a single unit ceramic restoration, and a final exam. The students also gave presentations about systems and materials used. The classroom exercises included small-group learning and class debate sessions.ResultsIn 2015, two 5-week sessions included a total of 16 participants (8 each). The overall course satisfaction from the combined two rounds was 4.7 out of 5. Comments for the course have been generally positive, indicating that the course was a successful introduction to CAD/CAM technology. Students' comments showed that they were very impressed by the new technology and interested in implementing it in their practices.DiscussionThis exercise in the comparison between CAD/CAM and conventional technology resulted in a deeper understanding of digital dentistry systems and ensured that students were prepared in their clinical reasoning to apply their education in real-world decision making after graduation. By integrating this new technology in a core curriculum, preclinical, prosthodontic sophomore course and as a junior elective course, students were provided with the hands-on experience needed to utilize CAD/CAM effectively in patient care.
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Project description:Facial asymmetries exist in all individuals. Due to these facial asymmetries that exist, a standardized approach in locating the occlusal plane that is parallel to the ala-tragus and interpupillary lines, may result in less than ideal esthetics in the final restoration. The challenge for the prosthodontist is to determine an acceptable occlusal plane with an individualized approach that can be used as a guide for alignment of the maxillary anterior teeth in cases that require their replacement or extensive restoration. The present study uses an inexpensive and standardized digital photographic technique along with computer assisted analysis to measure the asymmetries of the human face. Statistical Analysis used-Karl pearson's correlation coeffient was used. The correlation coefficient was then subjected to 't' test and 'p' value was used to find out the level of statistical significance. Left side of the face was found to be at a higher level than the right side.
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Project description:We investigated the workflow of computer-assisted mandibular reconstruction that was performed with a patient-specific mandibular reconstruction plate fabricated with computer-aided design and computer-aided manufacturing (CAD/CAM) techniques and a fibula flap. We assessed the feasibility of this technique from virtual planning to the completion of surgery. Computed tomography (CT) scans of a cadaveric skull and fibula were obtained for the virtual simulation of mandibular resection and reconstruction using ProPlan CMF software (Materialise(®)/DePuy Synthes(®)). The virtual model of the reconstructed mandible provided the basis for the computer-aided design of a patient-specific reconstruction plate that was milled from titanium using a five-axis milling machine and CAM techniques. CAD/CAM techniques were used for producing resection guides for mandibular resection and cutting guides for harvesting a fibula flap. Mandibular reconstruction was simulated in a cadaveric wet laboratory. No problems were encountered during the procedure. The plate was fixed accurately to the residual bone without difficulty. The fibula segments were attached to the plate rapidly and reliably. The fusion of preoperative and postoperative CT datasets demonstrated high reconstruction precision. Computer-assisted mandibular reconstruction with CAD/CAM-fabricated patient-specific reconstruction plates appears to be a promising approach for mandibular reconstruction. Clinical trials are required to determine whether these promising results can be translated into successful practice and what further developments are needed.
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Project description:Because of its complexity, the atrioventricular node (AVN), remains 1 of the least understood regions of the heart. The aim of the study was to construct a detailed anatomic model of the AVN and relate it to AVN function. The electric activity of a rabbit AVN preparation was imaged using voltage-dependent dye. The preparation was then fixed and sectioned. Sixty-five sections at 60- to 340-microm intervals were stained for histology and immunolabeled for neurofilament (marker of nodal tissue) and connexin43 (gap junction protein). This revealed multiple structures within and around the AVN, including transitional tissue, inferior nodal extension, penetrating bundle, His bundle, atrial and ventricular muscle, central fibrous body, tendon of Todaro, and valves. A 3D anatomically detailed mathematical model (approximately 13 million element array) of the AVN and surrounding atrium and ventricle, incorporating all cell types, was constructed. Comparison of the model with electric activity recorded in experiments suggests that the inferior nodal extension forms the slow pathway, whereas the transitional tissue forms the fast pathway into the AVN. In addition, it suggests the pacemaker activity of the atrioventricular junction originates in the inferior nodal extension. Computer simulation of the propagation of the action potential through the anatomic model shows how, because of the complex structure of the AVN, reentry (slow-fast and fast-slow) can occur. In summary, a mathematical model of the anatomy of the AVN has been generated that allows AVN conduction to be explored.
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Project description:After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States.To measure performance of digital screening mammography with and without CAD in US community practice.We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening.Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD.Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97).Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.
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Project description:Multimaterials deposition, a distinct advantage in bioprinting, overcomes material's limitation in hydrogel-based bioprinting. Multimaterials are deposited in a build/support configuration to improve the structural integrity of three-dimensional bioprinted construct. A combination of rapid cross-linking hydrogel has been chosen for the build/support setup. The bioprinted construct was further chemically cross-linked to ensure a stable construct after print. This paper also proposes a file segmentation and preparation technique to be used in bioprinting for printing freeform structures.
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Project description:ObjectivesUse of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if reference testing has not been performed in all individuals. We aimed to determine such thresholds through secondary analysis of the 2019 Lesotho national tuberculosis prevalence survey.MethodsSymptom screening and chest radiographs were performed in participants aged ≥15 years; those symptomatic or with abnormal chest radiographs provided samples for Xpert MTB/RIF and culture testing. Chest radiographs were processed using CAD4TB version 7. We used six methodological approaches to deal with participants who did not have bacteriological test results to estimate pulmonary tuberculosis prevalence and assess diagnostic accuracy.ResultsAmong 17 070 participants, 5214 (31%) had their tuberculosis status determined; 142 had tuberculosis. Prevalence estimates varied between methodological approaches (0.83-2.72%). Using multiple imputation to estimate tuberculosis status for those eligible but not tested, and assuming those not eligible for testing were negative, a CAD4TBv7 threshold of 13 had a sensitivity of 89.7% (95% CI 84.6-94.8) and a specificity of 74.2% (73.6-74.9), close to World Health Organization (WHO) target product profile criteria. Assuming all those not tested were negative produced similar results.ConclusionsThis is the first study to evaluate CAD4TB in a community screening context employing a range of approaches to account for unknown tuberculosis status. The assumption that those not tested are negative - regardless of testing eligibility status - was robust. As threshold determination must be context specific, our analytically straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants.
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Project description:To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system.IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs.The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters.Our study indicates the feasibility of the 3D approach to the detection of clustered microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.
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