Coordinate Values Research Articles

Lossless Watermarking Algorithm for Geographic Point Cloud Data Based on Vertical Stability

With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data.

Research on Crop 3D Model Reconstruction Based on RGB-D Binocular Vision

Taking maize seedlings as the object, the implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated in this research. First, multiple images are taken from different angles around the target. By mapping the maize seedling region coordinate values after the Otsu algorithm and global threshold segmentation to the corresponding depth image, the depth data of the maize seedling region can be obtained accurately. An improved mean filter is proposed to adaptively fill the holes in the depth image. Then, the different point clouds with the fixed step angle of the maize seedling are registered and fuzed. Finally, after the fusion point cloud is simplified, the 3D model of crops can be reconstructed. Experimental results show that the simplification effect of the octree algorithm is better than that of the voxel grid filter. Among all the step angles, the reconstruction error of the step angle with 60° is the smallest. Under this condition, the height error between the model and the maize seedling is 2.22%, and the error in stem diameter is 11.67%.

The Reliability of Three-Dimensional Landmark-Based Craniomaxillofacial and Airway Cephalometric Analysis.

Cephalometric analysis is a standard diagnostic tool in orthodontics and craniofacial surgery. Today, as conventional 2D cephalometry is limited and susceptible to analysis bias, a more reliable and user-friendly three-dimensional system that includes hard tissue, soft tissue, and airways is demanded in clinical practice. We launched our study to develop such a system based on CT data and landmarks. This study aims to determine whether the data labeled through our process is highly qualified and whether the soft tissue and airway data derived from CT scans are reliable. We enrolled 15 patients (seven males, eight females, 26.47 ± 3.44 years old) diagnosed with either non-syndromic dento-maxillofacial deformities or OSDB in this study to evaluate the intra- and inter-examiner reliability of our system. A total of 126 landmarks were adopted and divided into five sets by region: 28 cranial points, 25 mandibular points, 20 teeth points, 48 soft tissue points, and 6 airway points. All the landmarks were labeled by two experienced clinical practitioners, either of whom had labeled all the data twice at least one month apart. Furthermore, 78 parameters of three sets were calculated in this study: 42 skeletal parameters (23 angular and 19 linear), 27 soft tissue parameters (9 angular and 18 linear), and 9 upper airway parameters (2 linear, 4 areal, and 3 voluminal). Intraclass correlation coefficient (ICC) was used to evaluate the inter-examiner and intra-examiner reliability of landmark coordinate values and measurement parameters. The overwhelming majority of the landmarks showed excellent intra- and inter-examiner reliability. For skeletal parameters, angular parameters indicated better reliability, while linear parameters performed better for soft tissue parameters. The intra- and inter-examiner ICCs of airway parameters referred to excellent reliability. In summary, the data labeled through our process are qualified, and the soft tissue and airway data derived from CT scans are reliable. Landmarks that are not commonly used in clinical practice may require additional attention while labeling as they are prone to poor reliability. Measurement parameters with values close to 0 tend to have low reliability. We believe this three-dimensional cephalometric system would reach clinical application.

Enhancing human activity recognition using features reduction in IoT edge and Azure cloud

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These technologies have triggered huge traffic and delay by continuously transmitting telemetry data to the cloud. IoT edge choice made decision-making closer to the environment, which decreases traffic and speeds up response time. Human activity recognition (HAR) systems, among other IoT applications, require systems with quick response time; reduce costs using constrained traffic to the cloud while maintaining accurate prediction results. This study proposes an application of HAR for predicting activities using up to three smartphone accelerometers. Three models are developed, trained, and deployed to achieve the necessary accuracy at the IoT edge and in the cloud, with an acceptable response time. Since each coordinate value from the three accelerometers has different importance in activity category prediction, focusing on the data from the most related values can help minimize the amount of information transferred from the edge to the cloud. Six models were trained in the cloud; three were deployed and tested at the edge with different features by selecting the most important ones using Principal Component Analysis (PCA). Different experiments showed that traffic and processing time decreased significantly based on the time required to predict HAR categories with acceptable accuracy. Since there is significant latency between the edge and the cloud and within the cloud, sending samples for verification save bandwidth, and processing requests locally at the edge speed up predictions. Results illustrate that the time required to serve one request from the environment where smartphones generate traffic through the internet connection to the cloud took about 5.8 s on average, including transmission delays and the prediction process. During this time, the model at the edge can serve 150 requests with the same accuracy using nine features. In addition, the edge can serve 286 requests in 5.8 s with 94.8 % accuracy when choosing the top four features at the edge.

Estimation of Height Changes of Continuous GNSS Stations in the Eastern Anatolia Region during the Seasonal Variation

Estimating the height component of Global Navigation Satellite System (GNSS) stations is widely known to be more challenging than estimating the horizontal position. In this study, we utilized height time series data from 37 continuous GNSS stations that were part of the Turkish RTK CORS Network called TUSAGA-Active (Turkish National Permanent GNSS Network Active). The data covered the period from 2014 to 2019, and the selection of stations focused on the Eastern Anatolia region of Turkey due to its topographic characteristics and the pronounced influence of seasonal changes, which facilitated the interpretation of the effects on the height component. The daily coordinates of the GNSS stations were derived using the GAMIT/GLOBK software solution. We identified statistically significant trends, periodic variations, and stochastic components associated with the stations by applying time series analysis to these daily coordinate values. As a result, the vertical velocities of the GNSS stations were determined, along with their corresponding standard deviations. Furthermore, examining the height components of the continuous GNSS stations revealed seasonal effects. We aimed to investigate the potential relationship between these height components and meteorological parameters. The study provides evidence of the interconnectedness between the height components of continuous GNSS stations and various meteorological parameters. Simple linear regression analysis and ARMA time series modeling were utilized to establish this relationship.

Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis

Deep Learning algorithms are used by many different disciplines for various purposes, thanks to their ever-developing data processing skills. Convolutional neural network (CNN) are generally developed and used for this integration purpose. On the other hand, the widespread usage of Unmanned Aerial Vehicles (UAV) enables the collection of aerial photographs for Photogrammetric studies. In this study, these two fields were brought together and it was aimed to find the equivalents of the objects detected from the UAV images using deep learning in the global coordinate system and to evaluate their accuracy over these values. For these reasons, v3 and v4 versions of the YOLO algorithm, which prioritizes detecting the midpoint of the detected object, were trained in Google Colab’s virtual machine environment using the prepared data set. The coordinate values read from the orthophoto and the coordinate values of the midpoints of the objects, which were derived according to the estimations made by the YOLO-v3 and YOLOV4-CSP models, were compared and their spatial accuracy was calculated. Accuracy of 16.8 cm was obtained with the YOLO-v3 and 15.5 cm with the YOLOv4-CSP. In addition, the mAP value was obtained as 80% for YOLOv3 and 87% for YOLOv4-CSP. F1-score is 80% for YOLOv3 and 85% for YOLOv4-CSP.

Analytical Solution of the Schrödinger Equation with an Exponential Type Mass Depending on the Spatial Variable

In the present work, we proceed to study the Schrödinger equation with dependent mass position. From the resulting partial differential equations, we obtain exact analytical solutions governed by Bessel functions. The exact solution of Schrödinger's equation for a particle with dependent position of the mass (PDM) is a "half-harmonic potential" defined in a Hilbert space. The harmonic oscillator is carried by the wave function ψ(x) through the Bessel function. The magnitude of ψ(x) increases dramatically as the values of the spatial coordinate become larger and larger. This growth is all the more marked as the mass parameter increases. It appears that the wave function has the same asymptotic behavior as the solution of Airy's equation of the second kind. Their oscillations have the same amplitudes near infinity. The mass parameter has a great importance and influence on the mass and also on the wave potential. It is a control parameter in the Schrödinger equations (PDM).

Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over?

To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data. This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean. The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions. There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.

Integrating Coordinate Transformation and Random Sampling Into High-Capacity Reversible Data Hiding in Encrypted Polygonal Models

Reversible data hiding in encrypted media involves using algorithms to perform data encryption to improve the privacy of the original media and hide data for covert communication or access control. This study explores the feasibility of applying coordinate transformation and random sampling to enhance the embedding rate and total embedding capacity of separable reversible data hiding in encrypted polygonal models based on multiple most significant bit (multi-MSB) prediction and Huffman coding. We first transform each vertex coordinate value of the input model into a decimal value between 0 and 1 and then convert the value into numerous binary digits with a user-defined compression threshold. Thereafter, the random sampling concept is used to generate embeddable vertices with only partial neighboring vertices as a reference. Thus, the embedding rate and total embedding capacity can both be increased considerably. The multi-MSB prediction technique is adopted to obtain the embedding capacity of each embeddable vertex, each vertex coordinate value in the proposed study can have a respective embedding length. Finally, the auxiliary information compressed through Huffman coding and an encrypted secret message are embedded in the multi-MSB of each embeddable vertex coordinate value through bit substitution. The experimental results of this study indicate the feasibility of the proposed algorithm.

한글 모음 및 표정 변화에 따른 안면 분석 시스템

Face deformation is caused by various causes such as facial paralysis or asymmetry. Therefore, accurate cause analysis and diagnosis are required, but CT, MRI, and Nottingham Grading System are subjective in nature and are affected by clinician proficiency and fatigue. In order to reduce the time and cost burden of patients who have to continuously visit specialized institutions for treatment, this study aims to develop a facial analysis system according to changes in Korean vowels and facial expressions. First, the coordinate value of the face landmark was extracted based on the webcam image, and then the distance value between the left and right face landmarks was calculated. In addition, quantification was performed based on the actual length of the eyes and lips and the results of the system derivation, and the final system analysis results could be checked with figures and charts.

Value-creating practices and barriers for collaboration between designers and artisans: a systematic literature review

ABSTRACT This study aims to map out the challenges of existing design collaborations in craft sectors and delineate different types of value associated with artisan and designer collaboration. A systematic literature review was undertaken, examining peer-reviewed journal articles published between 2000 and 2022. The results indicate three levels of sustainable value created from designer-artisan design collaboration, which include business growth, regional development, and autonomic capability development. However, collaborative value is often hindered by a lack of a clear understanding of the process of value coordination or shared value creation, the challenges of balancing different relationships, thinking patterns, and values, and a lack of understanding of different actors’ roles regarding materials, technology, and design processes. This study bridges knowledge gaps by offering a holistic depiction of designer-artisan design collaboration, mapping current challenges of practices, and defining future research agendas.

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Identifying lane markings is a key technology in assisted driving and autonomous driving. The traditional sliding window lane detection algorithm has good detection performance in straight lanes and curves with small curvature, but its detection and tracking performance is poor in curves with larger curvature. Large curvature curves are common scenes in traffic roads. Therefore, in response to the problem of poor lane detection performance of traditional sliding window lane detection algorithms in large curvature curves, this article improves the traditional sliding window algorithm and proposes a sliding window lane detection calculation method, which integrates steering wheel angle sensors and binocular cameras. When a vehicle first enters a bend, the curvature of the bend is not significant. Traditional sliding window algorithms can effectively detect the lane line of the bend and provide angle input to the steering wheel, enabling the vehicle to travel along the lane line. However, as the curvature of the curve increases, traditional sliding window lane detection algorithms cannot track lane lines well. Considering that the steering wheel angle of the car does not change much during the adjacent sampling time of the video, the steering wheel angle of the previous frame can be used as input for the lane detection algorithm of the next frame. By using the steering wheel angle information, the search center of each sliding window can be predicted. If the number of white pixels within the rectangular range centered around the search center is greater than the threshold, the average of the horizontal coordinate values of these white pixels will be used as the horizontal coordinate value of the sliding window center. Otherwise, the search center will be used as the center of the sliding window. A binocular camera is used to assist in locating the position of the first sliding window. The simulation and experimental results show that compared with traditional sliding window lane detection algorithms, the improved algorithm can better recognize and track lane lines with large curvature in bends.

Characterization of plant-based meat alternatives blended with anthocyanins, chlorophyll, and various edible natural pigments

ABSTRACT The objective of this study was to characterize the physicochemical, proximal, textural, and visual properties of plant-based meat (PBM) patties blended with natural pigments such as anthocyanin from black beans, Fe-chlorophyll from spinach, and edible red and purple pigments. A comparison was made between these plant-based pigments and the pigments derived from animal-based sources (control). The values of the color coordinates of the developed patties were broadly distributed (L* [lightness], a* [redness], and b* [yellowness] of raw PBMs: 34–53, 2–25, and 4–27; steamed: 29–42, 4–21, and 5–25; and cooked: 28–43, 5–19, and 9–21, respectively). The proximate chemical analysis revealed no (P > .05) significant difference between the PBMs and the control in terms of moisture (63.28–61.25), crude protein (20.98–19.17) crude fat (3.19–2.92) and ash content (3.80–1.71). Nevertheless, significant differences (P < .05) were observed in textural attributes among all samples (S1-S16) in terms of hardness, chewiness, and gumminess. For instance, hardness (6.39–3.72), chewiness (9.27–4.13), and gumminess (3.04–1.60). Conversely, springiness showed considerable variations (P < .05) in a majority of PBM samples except for S1 (dilute red) (3.06) and S4 (paprika) (3.06) than control (3.31). For DPPH radical scavenging activity of patties samples incorporated with, the S10 (Red Cabbage) (19.95), S13 (Grape skin Color) (24.35), S15 (Anthocyanin) (33.12), and S16 (Fe-pheophytin) (18.69) of PBMs had significantly higher activity (P < .05) than the control (5.14). To sum up, the inclusion of natural pigments in PBM resulted in improved overall properties without any deleterious effects.

Tunable white light photoluminescence of a single phase Tm3+/Tb3+/Eu3+ codoped GdPO4 phosphor

The present study describes the production of triply-doped GdPO4 and its optical characteristics. Structural information of the prepared material has been investigated by using X-ray diffraction and Fourier transform infrared spectroscopy. The material synthesized crystallizes into a single monoclinic crystal of space group P 21/n. The excitation curves of photoluminescence of single doped GdPO4: Tm3+, Tb3+, and Eu3+ are recorded and their emission spectra are analyzed. Triple-doped material emits multi colour light after a single UV stimulation. White light emission was seen in both triply-doped GdPO4 with variable Tm3+ concentration and triply-doped GdPO4 with variable Eu3+ concentration. GdPO4 doped with Tb3+ (7 mol%), Tm3+ (1.5 mol%), and Eu3+ (2 mol%) has colour coordinates (x = 0.3290, y = 0.3291) approximately near the value of typical white colour coordinates (x = 0.333, y = 0.3333). Furthermore, the transfer of energy in Tm3+, Tb3+ and Eu3+ in triple doped phosphor with varying Eu3+ concentrations was explained. Electric dipole-dipole interaction is used to justify and describe the process of energy transfer from Tb3+ to Eu3+. Decay time measurements were performed to explain energy transfer. Colour rendering index (CRI), Corelated colour temperature (CCT), Quantum efficiency and thermal stability of the prepared phosphor was calculated. All these parameters infer that GdPO4 doped with Tm3+, Tb3+ and Eu3+ may have potential application in white LEDs.

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.

Effect of inductor position on thermal characteristics during induction lap welding process for Fe/Al dissimilar metal

In order to investigate the influence of the inductor position on thermal characteristics during induction lap welding (ILW) process of steel-aluminum heterogeneous metals, the finite element model of ILW process was established. The effect of X-coordinate and Z-coordinate on the magnetic flux density, heat affected zone (HAZ) and temperature uniformity during ILW process was studied. The results show that a relatively small Z-coordinate value increases the maximum magnetic flux density and the maximum temperature of the workpiece in the process of ILW on Fe/Al dissimilar metal, reduces the time to reach the thermal equilibrium, and the temperature uniformity is relatively low. A relatively small X-coordinate value reduces the maximum magnetic flux density and the maximum temperature of the workpiece in the process of ILW on Fe/Al dissimilar metal, increases the time to reach the thermal equilibrium, and the temperature uniformity is relatively high. The Z-direction coordinate value (change of proximity effect) has a greater influence on the electromagnetic and temperature fields during ILW than the X-direction coordinate value (change of magnetic shielding region). The simulated temperature curve with time and the HAZ distribution under different process parameters agree with the experimental results, which proves the reliability of the numerical model.

Study on the method of automatically determining maxillary complex landmarks based on non-rigid registration algorithms

Objective: To explore an automatic landmarking method for anatomical landmarks in the three-dimensional (3D) data of the maxillary complex and preliminarily evaluate its reproducibility and accuracy. Methods: From June 2021 to December 2022, spiral CT data of 31 patients with relatively normal craniofacial morphology were selected from those who visited the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The sample included 15 males and 16 females, with the age of (33.3±8.3) years. The maxillary complex was reconstructed in 3D using Mimics software, and the resulting 3D data of the maxillary complex was mesh-refined using Geomagic software. Two attending physicians and one associate chief physician manually landmarked the 31 maxillary complex datasets, determining 24 anatomical landmarks. The average values of the three expert landmarking results were used as the expert-defined landmarks. One case that conformed to the average 3D morphological characteristics of healthy individuals' craniofacial bones was selected as the template data, while the remaining 30 cases were used as target data. The open-source MeshMonk program (a non-rigid registration algorithm) was used to perform an initial alignment of the template and target data based on 4 landmarks (nasion, left and right zygomatic arch prominence, and anterior nasal spine). The template data was then deformed to the shape of the target data using a non-rigid registration algorithm, resulting in the deformed template data. Based on the unchanged index property of homonymous landmarks before and after deformation of the template data, the coordinates of each landmark in the deformed template data were automatically retrieved as the automatic landmarking coordinates of the homonymous landmarks in the target data, thus completing the automatic landmarking process. The automatic landmarking process for the 30 target data was repeated three times. The root-mean-square distance (RMSD) of the dense corresponding point pairs (approximately 25 000 pairs) between the deformed template data and the target data was calculated as the deformation error of the non-rigid registration algorithm, and the intra-class correlation efficient (ICC) of the deformation error in the three repetitions was analyzed. The linear distances between the automatic landmarking results and the expert-defined landmarks for the 24 anatomical landmarks were calculated as the automatic landmarking errors, and the ICC values of the 3D coordinates in the three automatic landmarking repetitions were analyzed. Results: The average three-dimensional deviation (RMSD) between the deformed template data and the corresponding target data for the 30 cases was (0.70±0.09) mm, with an ICC value of 1.00 for the deformation error in the three repetitions of the non-rigid registration algorithm. The average automatic landmarking error for the 24 anatomical landmarks was (1.86±0.30) mm, with the smallest error at the anterior nasal spine (0.65±0.24) mm and the largest error at the left oribital (3.27±2.28) mm. The ICC values for the 3D coordinates in the three automatic landmarking repetitions were all 1.00. Conclusions: This study established an automatic landmarking method for three-dimensional data of the maxillary complex based on a non-rigid registration algorithm. The accuracy and repeatability of this method for landmarking normal maxillary complex 3D data were relatively good.

Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks.

Geometrical assessments of human skulls have been conducted based on anatomical landmarks. If developed, the automatic detection of these landmarks will yield both medical and anthropological benefits. In this study, an automated system with multi-phased deep learning networks was developed to predict the three-dimensional coordinate values of craniofacial landmarks. Computed tomography images of the craniofacial area were obtained from a publicly available database. They were digitally reconstructed into three-dimensional objects. Sixteen anatomical landmarks were plotted on each of the objects, and their coordinate values were recorded. Three-phased regression deep learning networks were trained using ninety training datasets. For the evaluation, 30 testing datasets were employed. The 3D error for the first phase, which tested 30 data, was 11.60 px on average (1 px = 500/512 mm). For the second phase, it was significantly improved to 4.66 px. For the third phase, it was further significantly reduced to 2.88. This was comparable to the gaps between the landmarks, as plotted by two experienced practitioners. Our proposed method of multi-phased prediction, which conducts coarse detection first and narrows down the detection area, may be a possible solution to prediction problems, taking into account the physical limitations of memory and computation.

Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.

Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical setting remain concerns. The aim of this study was to investigate the feasibility of a deep learning-based segmentation model for measuring ISM in patients who underwent ACDF surgery. This study is a retrospective analysis of flexion-extension dynamic cervical radiographs from a single institution and a validation of a convolutional neural network (CNN)-based artificial intelligence (AI) algorithm for measuring ISM. Data from 150 lateral cervical radiographs from the normal adult population were used to train the AI algorithm. A total of 106 pairs of dynamic flexion-extension radiographs from patients who underwent ACDF at a single institution were analyzed and validated for measuring ISM. To evaluate the agreement power between human experts and the AI algorithm, the authors assessed the interrater reliability using the intraclass correlation coefficient and root mean square error (RMSE) and performed a Bland-Altman plot analysis. They processed 106 pairs of radiographs from ACDF patients into the AI algorithm for autosegmenting the spinous process created using 150 normal population radiographs. The algorithm automatically segmented the spinous process and converted it to a binary large object (BLOB) image. The rightmost coordinate value of each spinous process from the BLOB image was extracted, and the pixel distance between the upper and lower spinous process coordinate value was calculated. The AI-measured ISM was calculated by multiplying the pixel distance by the pixel spacing value included in the DICOM tag of each radiograph. The AI algorithm showed a favorable prediction power for detecting spinous processes with an accuracy of 99.2% in the test set radiographs. The interrater reliability between the human and AI algorithm of ISM was 0.88 (95% CI 0.83-0.91), and its RMSE was 0.68. In the Bland-Altman plot analysis, the 95% limit of interrater differences ranged from 0.11 to 1.36 mm, and a few observations were outside the 95% limit. The mean difference between observers was 0.02 ± 0.68 mm. This novel CNN-based autosegmentation algorithm for measuring ISM in dynamic cervical radiographs showed strong agreement power to expert human raters and could help clinicians to evaluate segmental motion following ACDF surgery in clinical settings.

Multidisciplinary prostate cancer consultation at home: Does the virtual visit platform adversely affect care for elderly patients or impact treatment recommendations?

1521 Background: Amidst the COVID-19 pandemic, Hartford Healthcare (HHC) implemented a multidisciplinary virtual visit prostate cancer clinic (MDVV) to allow patients to receive cancer consultations at home. This clinic has shown significant logistical advantages for provision of coordinated cancer care. To evaluate concerns regarding usability of the telehealth platform for elderly patients, and the absence of in person physical exam at initial consult, we evaluated the MDVV in terms of patient satisfaction based on age, as well as for differences in treatment recommendations after patients were seen in-person at a pre-treatment follow up visit. Methods: Patients with newly diagnosed or progressive prostate cancer were offered a MDVV consultation. Those who assented had sequential virtual consultations with a urologic surgeon, a radiation oncologist, and a medical oncologist, followed by a group conference with all 3 specialists, and were guided through the visit by a nurse navigator. Patients then completed a 7 item satisfaction survey using a 5 point Likert scale evaluating their perception of the efficacy, efficiency, coordination, and overall value of the clinic. Responses were stratified by age group and compared. Chart review determined the impact of subsequent in person office visit on treatment recommendations, and the proportion of patients who underwent staging imaging according to NCCN guidelines. This was compared with a cohort of consecutive patients who completed traditional consults with two high volume prostate cancer surgeons immediately before the pandemic. Results: A total of 161 patients with newly diagnosed or progressive prostate cancer received a MDVV consultation from August 2020 – February 2023. Patient age ranged from 47-84 years (9% &lt; 60 yrs, 52% 60-70 yrs, 34% 70-80 yrs, 5% &gt;80 yrs). The median score for patient satisfaction on all 7 domains was 1 (strongly agree), and did not differ between age groups. There were no instances of a change in treatment recommendation after follow up in person office visit. While 100% of the MDVV cohort received multidisciplinary consultation before treatment, only 14/50 traditional cohort patients (28%) ultimately received the same (p&lt;.001). 100% of MDVV patients underwent imaging according to NCCN guidelines, compared with 33/50 (66%) of traditional patients (p&lt;.001). Conclusions: A virtual format for a multidisciplinary prostate cancer clinic is a logistically advantageous, effective means of offering in-home consultation to prostate cancer patients, leading to high patient satisfaction, regardless of age. The absence of in-person physical examination at initial consultation did not ultimately impact treatment recommendations. This format also improved access to multidisciplinary consultation and increased adherence to imaging guidelines.