OBJECTIVE: This study aimed to assess the fit of interim crowns produced using DLP-based 3D printing with different manufacturing workflows-open and proprietary-versus milling technology.
METHODS: A total of 120 crowns were evaluated using the replica technique. The control group (Mill, n = 30) was manufactured via subtractive technology. Experimental groups were printed using a DLP printer (SprintRay Pro95). In the proprietary mode (SR100, n = 30), manufacturer resin was used with a 100-μm layer thickness (LT) and a splashing cleaning postprocessing. In the open mode, validated resin was used. Group B100 (n = 30) had a 100-μm LT, and group B50 (n = 30) had a 50-μm followed by postprocessing in an ultrasonic bath with full immersion in isopropyl alcohol. Kruskal-Wallis tests with Bonferroni correction was applied after normal analysis (α = 0.05).
RESULTS: Group B50 exhibited the best overall fit (123.87 ± 67.42 μm), which was comparable to the gold standard Milling group, which demonstrated the lowest marginal fit (p = 0.760). SR100 showed significantly poorer performance compared to Mill, B50, and B100 (p < 0.001).
CONCLUSIONS: 3D printed and milled interim crowns generally demonstrated clinically acceptable fit, with the exception of the SR100 group. Postprocessing notably influenced crown fit, with the open mode with total immersion in isopropyl alcohol being superior.
CONCLUSIONS: The present study demonstrates that the selection of an optimal manufacturing and postprocessing workflow results in superior fit for interim crowns. This enables dental professionals to evaluate protocols and ensure reliable outcomes with improved clinical outcomes in interim crown fabrication.

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences.

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person\'s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.

OBJECTIVE: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP).
METHODS: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated.
RESULTS: A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001).
CONCLUSIONS: The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.

The development of innovative production processes and the optimization of photobioreactors play an important role in generating industrial competitive production technologies for phototrophic biofilms. With emerse photobioreactors a technology was introduced that allowed efficient surface attached cultivation of terrestrial cyanobacteria. However, the productivity of emerse photobioreactors depends on the available cultivation surface. By the implementation of biocarriers to the bioreactor volume, the cultivation surface can be increased which potentially improves productivity and thus the production of valuable compounds. To investigate the surface attached cultivation on biocarriers new photobioreactors need to be developed. Additive manufacturing (AM) offers new opportunities for the design of photobioreactors but producing the needed transparent parts can be challenging using AM techniques. In this study an emerse fixed bed photobioreactor was designed for the use of biocarriers and manufactured using different AM processes. To validate the suitability of the photobioreactor for phototrophic cultivation, the optical properties of three-dimensional (3D)-printed transparent parts and postprocessing techniques to improve luminous transmittance of the components were investigated. We found that stereolithography 3D printing can produce parts with a high luminous transmittance of over 85% and that optimal postprocessing by sanding and clear coating improved the clarity and transmittance to more than 90%. Using the design freedom of AM resulted in a bioreactor with reduced part count and improved handling. In summary, we found that modern 3D-printing technologies and materials are suitable for the manufacturing of functional photobioreactor prototypes.

OBJECTIVE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques.
METHODS: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes.
RESULTS: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%.
CONCLUSIONS: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.

B-mode ultrasound is routinely performed to evaluate the prostate gland in neutered dogs, although, the detection of malignancies may be challenging. Contrast-enhanced ultrasound (CEUS) has shown to be useful for the assessment of prostatic perfusion in normal and diseased dogs, although the interpretation of contrast ultrasonographic features may still be subjective. A quantitative tool for evaluating prostatic perfusion might improve the reliability of the results in terms of early detection of prostate neoplasia in neutered dogs. The present study aimed to evaluate the applicability of a postprocessing analysis tool to CEUS of the prostate in healthy neutered dogs, to provide quantitative measurements, and to study the influence of individual characteristics on prostatic regression. Twenty-three neutered dogs underwent a B-mode and CEUS examination of the prostate to acquire data about prostatic morphology and microcirculation. The prostate was imaged using a 5-7.5 MHz linear transducer and contrast was administered intravenously. Videoclips were analyzed by using Qontrast software and a postprocessing digital analysis tool (ImageJ) to measure perfusion peak intensity, time to peak, and vascularization ratio at the moment of the peak, which were then related to body weight, age, and time elapsed since orchiectomy. Correlation tests revealed higher vascularization in younger compared with older dogs (P < .05) and in smaller compared with larger dogs (P < .05). Time elapsed since orchiectomy (P > .05) did not affect prostatic perfusion. Contrast-enhanced ultrasound and the postprocessing analysis tool ImageJ allowed analysis of vascular perfusion in all dogs and have the potential to improve the diagnostic possibilities for andrological examination.

The rapid advancement of three-dimensional (3D) printing (i.e., a type of additive manufacturing) technology has brought about significant advances in various industries, including the food industry. Among its many potential benefits, 3D food printing offers a promising solution to deliver products meeting the unique nutritional needs of diverse populations while also promoting sustainability within the food system. However, this is an emerging field, and there are several aspects to consider when planning for use of 3D food printing for large-scale food production. This comprehensive review explores the importance of food safety when using 3D printing to produce food products, including pathogens of concern, machine hygiene, and cleanability, as well as the role of macronutrients and storage conditions in microbial risks. Furthermore, postprocessing factors such as packaging, transportation, and dispensing of 3D-printed foods are discussed. Finally, this review delves into barriers of implementation of 3D food printers and presents both the limitations and opportunities of 3D food printing technology.

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver\'s safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13534-023-00313-8.