Comparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution

Craniofacial identification standards: A review of reliability, reproducibility, and implementation

There are numerous anatomical and anthropometrical standards that can be utilised for craniofacial analysis and identification. These standards originate from a wide variety of sources, such as orthodontic, maxillofacial, surgical, anatomical, anthropological and forensic literature, and numerous media have been employed to collect data from living and deceased subjects. With the development of clinical imaging and the enhanced technology associated with this field, multiple methods of data collection have become accessible, including Computed Tomography, Cone-Beam Computed Tomography, Magnetic Resonance Imaging, Radiographs, Three-dimensional Scanning, Photogrammetry and Ultrasound, alongside the more traditional in vivo methods, such as palpation and direct measurement, and cadaveric human dissection. Practitioners often struggle to identify the most appropriate standards and research results are frequently inconsistent adding to the confusion. This paper aims to clarify how practitioners can choose optimal standards, which standards are the most reliable and when to apply these standards for craniofacial identification. This paper describes the advantages and disadvantages of each mode of data collection and collates published research to review standards across different populations for each facial feature. This paper does not aim to be a practical instruction paper; since this field encompasses a wide range of 2D and 3D approaches (e.g., clay sculpture, sketch, automated, computer-modelling), the implementation of these standards is left to the individual practitioner.

An evaluation of consumer smartphones for generating bolus and surface mould applicators for radiation oncology

BACKGROUND

The use of Computed Tomography (CT) imaging data to create 3D printable patient-specific devices for radiation oncology purposes is already well established in the literature and has shown to have superior conformity than conventional methods. Using non-ionizing radiation imaging techniques such as photogrammetry or laser scanners in-lieu of a CT scanner presents many desirable benefits including reduced imaging dose and fabrication of the device can be completed prior to simulation. With recent advancements in smartphone-based technology, photographic and LiDAR-based technologies are more readily available than ever before and to a high level of quality. As a result, these non-ionizing radiation imaging methods are now able to generate patient-specific devices that can be acceptable for clinical use.

PURPOSE

In this work, we aim to determine if smartphones can be used by radiation oncologists or other radiation oncology staff to generate bolus or brachytherapy surface moulds instead of conventional CT with equivalent or comparable accuracy.

METHODS

This work involved two separate studies: a phantom and participant study. For the phantom study, a RANDO anthropomorphic phantom (limited to the nose region) was used to generate 3D models based on three different imaging techniques: conventional CT, photogrammetry & LiDAR which were both acquired on a smartphone. Virtual boli were designed in Blender and 3D printed from PLA plastic material. The conformity of each printed boli was assessed by measuring the air gap volume and approximate thickness between the phantom & bolus acquired together on a CT. For the participant study, photographs, and a LiDAR scan of four volunteers were captured using an iPhone 13 Pro™ to assess their feasibility for generating human models. Each virtual 3D model was visually assessed to identify any issues in their reconstruction. The LiDAR models were registered to the photogrammetry models where a distance to agreement analysis was performed to assess their level of similarity. Additionally, a 3D virtual bolus was designed and printed using ABS material from all models to assess their conformity onto the participants skin surface using a verbal feedback method.

RESULTS

The photogrammetry derived bolus showed comparable conformity to the CT derived bolus while the LiDAR derived bolus showed poorer conformity as shown by their respective air gap volume and thickness measurements. The reconstruction quality of both the photogrammetry and LiDAR models of the volunteers was inadequate in regions of facial hair and occlusion, which may lead to clinically unacceptable patient-specific device that are created from these areas. All participants found the photogrammetry 3D printed bolus to conform to their nose region with minimal room to move while three of the four participants found the LiDAR was acceptable and could be positioned comfortably over their entire nose.

CONCLUSIONS

Smartphone-based photogrammetry and LiDAR software show great potential for future use in generating 3D reference models for radiation oncology purposes. Further investigations into whether they can be used to fabricate clinically acceptable patient-specific devices on a larger and more diverse cohort of participants and anatomical locations is required for a thorough validation of their clinical usefulness.

Instant Segmentation and Fitting of Excavations in Subsurface Utility Engineering

Using augmented reality for subsurface utility engineering (SUE) has benefited from recent advances in sensing hardware, enabling the first practical and commercial applications. However, this progress has uncovered a latent problem - the insufficient quality of existing SUE data in terms of completeness and accuracy. In this work, we present a novel approach to automate the process of aligning existing SUE databases with measurements taken during excavation works, with the potential to correct the deviation from the as-planned to as-built documentation, which is still a big challenge for traditional workers at sight. Our segmentation algorithm performs infrastructure segmentation based on the live capture of an excavation on site. Our fitting approach correlates the inferred position and orientation with the existing digital plan and registers the as-planned model into the as-built state. Our approach is the first to circumvent tedious postprocessing, as it corrects data online and on-site. In our experiments, we show the results of our proposed method on both synthetic data and a set of real excavations.

"Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry"

Background

The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.

Methods

30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland-Altman analysis, and calculation of the intraclass correlation coefficient (ICC).

Results

All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland-Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.

Conclusion

Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.

Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis

Due to the growing interest in climbing, increasing importance has been given to research in the field of non-invasive, camera-based motion analysis. While existing work uses invasive technologies such as wearables or modified walls and holds, or focuses on competitive sports, we for the first time present a system that uses video analysis to automatically recognize six movement errors that are typical for novices with limited climbing experience. Climbing a complete route consists of three repetitive climbing phases. Therefore, a characteristic joint arrangement may be detected as an error in a specific climbing phase, while this exact arrangement may not considered to be an error in another climbing phase. That is why we introduced a finite state machine to determine the current phase and to check for errors that commonly occur in the current phase. The transition between the phases depends on which joints are being used. To capture joint movements, we use a fourth-generation iPad Pro with LiDAR to record climbing sequences in which we convert the climber's 2-D skeleton provided by the Vision framework from Apple into 3-D joints using the LiDAR depth information. Thereupon, we introduced a method that derives whether a joint moves or not, determining the current phase. Finally, the 3-D joints are analyzed with respect to defined characteristic joint arrangements to identify possible motion errors. To present the feedback to the climber, we imitate a virtual mentor by realizing an application on the iPad that creates an analysis immediately after the climber has finished the route by pointing out the detected errors and by giving suggestions for improvement. Quantitative tests with three experienced climbers that were able to climb reference routes without any errors and intentionally with errors resulted in precision-recall curves evaluating the error detection performance. The results demonstrate that while the number of false positives is still in an acceptable range, the number of detected errors is sufficient to provide climbing novices with adequate suggestions for improvement. Moreover, our study reveals limitations that mainly originate from incorrect joint localizations caused by the LiDAR sensor range. With human pose estimation becoming increasingly reliable and with the advance of sensor capabilities, these limitations will have a decreasing impact on our system performance.

Characterization of the iPhone LiDAR-Based Sensing System for Vibration Measurement and Modal Analysis

Portable depth sensing using time-of-flight LiDAR principles is available on iPhone 13 Pro and similar Apple mobile devices. This study sought to characterize the LiDAR sensing system for measuring full-field vibrations to support modal analysis. A vibrating target was employed to identify the limits and quality of the sensor in terms of noise, frequency, and range, and the results were compared to a laser displacement transducer. In addition, properties such as phone-to-target distance and lighting conditions were investigated. It was determined that the optimal phone-to-target distance range is between 0.30 m and 2.00 m. Despite an indicated sampling frequency equal to the 60 Hz framerate of the RGB camera, the LiDAR depth map sampling rate is actually 15 Hz, limiting the utility of this sensor for vibration measurement and presenting challenges if the depth map time series is not downsampled to 15 Hz before further processing. Depth maps were processed with Stochastic Subspace Identification in a Monte Carlo manner for stochastic modal parameter identification of a flexible steel cantilever. Despite significant noise and distortion, the natural frequencies were identified with an average difference of 1.9% in comparison to the laser displacement transducer data, and high-resolution mode shapes including uncertainty ranges were obtained and compared to an analytical solution counterpart. Our findings indicate that mobile LiDAR measurements can be a powerful tool in modal identification if used in combination with prior knowledge of the structural system. The technology has significant potential for applications in structural health monitoring and diagnostics, particularly where non-contact vibration sensing is useful, such as in flexible scaled laboratory models or field scenarios where access to place physical sensors is challenging.

Measuring tree diameter using a LiDAR-equipped smartphone: a comparison of smartphone- and caliper-based DBH

Tree diameter measurement is one of the most important stages of forest inventories to assess growing stock, aboveground biomass, and landscape restoration options, among others. This study investigates the accuracy of measuring tree diameters using a Light Detection and Ranging (LiDAR)-equipped smartphone vs. a normal caliper (reference data) and the opportunity to use low-cost smartphone-based applications in forest inventories. To estimate the diameter at breast height (DBH) of single trees, we used a smartphone with a third-party app that automatically analyzed three-dimensional (3D) point clouds. For two different measurement techniques, we compared the two measurement techniques based on DBH data from 55 Calabrian pine (Pinus brutia Ten.) and 50 oriental plane (Platanus orientalis L.) trees using the paired-sample t-test and Wilcoxon signed-rank test. Mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), percent bias (PBIAS), and coefficient of determination (R²) were used as precision and error statistics. Statistical differences were observed between the reference and smartphone-based DBH according to the paired-sample t-test and Wilcoxon signed-rank test. The R² values obtained were determined as 0.91, 0.88, and 0.88 for Calabrian pine, oriental plane, and all tree species (105 trees), respectively. In addition to the overall accuracy performance of the comparison between reference and estimations, MAE, MSE, RMSE, and PBIAS values for the DBH of 105 tree stems were calculated as 1.56 cm, 5.42 cm, 2.33 cm, and - 5.10%, respectively. The estimation accuracies increased in regular stem forms compared with forked stems particularly observed on plane trees. Further experiments are needed to investigate the uncertainties associated with trees of different stem forms, species (coniferous or deciduous), different work environments, and different types of LiDAR and LiDAR-based app scanners.

Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Accuracy (trueness and precision) of four tablet-based applications for three-dimensional facial scanning: an in-vitro study.: Tablet-based applications for 3D facial scanning

OBJECTIVES

This study aimed to investigate the overall and regional accuracy (trueness and precision) of digital three-dimensional (3D) facial scans obtained from four tablet-based applications, which were (Bellus) the Bellus Dental Pro® (Bellus3D, Inc. Campbell, CA, USA), (Capture) the Capture®: 3D Scan Anything (Standard Cyborg, Inc. San Francisco, CA, USA), (Heges) the Heges® (by Marek Simonik, Ostrava, North Moravia, Czech Republic), and (Scandy) the Scandy Pro 3D Scanner® (Scandy LLC, New Orleans, LA, USA).

METHODS

A mannequin's face was marked with 63 landmarks. Subsequently, it was scanned 5 times using each scan application on an iPad Pro® (Apple Inc., Cupertino, CA, USA). The digital measurements were obtained with MeshLab® (CNR-ISTI, Pisa, Tuscany, Italy) and compared to the manual measurements using a digital vernier caliper (Truper Herramientas S.A., Colonia Granada, Mexico City, Mexico). The absolute mean difference and the standard deviation of the dimensional discrepancies were calculated. Moreover, the data were analysed by using one-way ANOVA, Levene's test, and Bonferroni´s correction.

RESULTS

The absolute mean trueness values were Bellus 0.41 ± 0.35 mm, Capture 0.38 ± 0.37 mm, Heges 0.39 ± 0.38 mm, and Scandy 0.47 ± 0.44 mm. Moreover, precision values were Bellus 0.46 mm, Capture 0.46 mm, Heges 0.54 mm, and Scandy 0.64 mm. Comparing the regions, Capture and Scandy showed the highest absolute mean difference, which was 0.81 mm in the Frontal and Zygomaticofacial regions, respectively.

CONCLUSIONS

The trueness and precision of all four tablet-based applications were clinically acceptable for diagnosis and treatment planning.

CLINICAL SIGNIFICANCE

The future of the three-dimensional facial scan is auspicious, and it has the potential to be affordable, accurate, and of great value for clinicians in their daily practice.

Use of a Three-dimensional Smartphone Scanner in the Quantitative Analysis of Repaired Unilateral Cleft Lip during the Coronavirus Disease Pandemic

Cleft lip, a major craniofacial abnormality, is highly prevalent among people with low socioeconomic status in Indonesia. Direct two-dimensional measurement of the affected region is the gold standard for surgical preparation; however, its compliance and usability are limited in pediatric patients. Modern smartphones, including iPhones, are equipped with high-resolution cameras, which can record images and videos of a face. Here, we investigated whether a three-dimensional (3D) smartphone scanner can be used for the facial measurements of patients with unilateral cleft lip.

A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.

Smartphone LiDAR Data: A Case Study for Numerisation of Indoor Buildings in Railway Stations

The combination of LiDAR with other technologies for numerisation is increasingly applied in the field of building, design, and geoscience, as it often brings time and cost advantages in 3D data survey processes. In this paper, the reconstruction of 3D point cloud datasets is studied, through an experimental protocol evaluation of new LiDAR sensors on smartphones. To evaluate and analyse the 3D point cloud datasets, different experimental conditions are considered depending on the acquisition mode and the type of object or surface being scanned. The conditions allowing us to obtain the most accurate data are identified and used to propose which acquisition protocol to use. This protocol seems to be the most adapted when using these LiDAR sensors to digitise complex interior buildings such as railway stations. This paper aims to propose: (i) a methodology to suggest the adaptation of an experimental protocol based on factors (distance, luminosity, surface, time, and incidence) to assess the precision and accuracy of the smartphone LiDAR sensor in a controlled environment; (ii) a comparison, both qualitative and quantitative, of smartphone LiDAR data with other traditional 3D scanner alternatives (Faro X130, VLX, and Vz400i) while considering three representative building interior environments; and (iii) a discussion of the results obtained in a controlled and a field environment, making it possible to propose recommendations for the use of the LiDAR smartphone at the end of the numerisation of the interior space of a building.

A Health App for Post-Pandemic Years (HAPPY) for people with physiological and psychosocial distress during the post-pandemic era: Protocol for a randomized controlled trial

Objective

This article describes a protocol for a randomized controlled trial to evaluate the effects of a three-level Health App for Post-Pandemic Years (HAPPY) on alleviating post-pandemic physiological and psychosocial distress.

Methods

Convenience and snowball sampling methods will be used to recruit 814 people aged 18+ with physiological and/or psychosocial distress. The experimental group will receive a 24-week intervention consisting of an 8-week regular supervision phase and a 16-week self-help phase. Based on their assessment results, they will be assigned to receive interventions on mindfulness, energy conservation techniques, or physical activity training. The waitlist control group will receive the same intervention in Week 25. The primary outcome will be changes in psychosocial distress, measured using the Kessler Psychological Distress Scale (K10). Secondary outcomes will include changes in levels of fatigue (Chinese version of the Brief Fatigue Inventory), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index), pain intensity (Numeric Rating Scale), positive appraisal (Short version of the 18-item Cognitive Emotion Regulation Questionnaire), self-efficacy (Chinese version of the General Self-efficacy Scale), depression and anxiety (Chinese version of the 21-item Depression Anxiety Stress Scale), and event impact (Chinese version of the 22-item Impact of Event Scale-Revised). All measures will be administered at baseline (T0), Week 8 after the supervision phase (T1), and 24 weeks post-intervention (T2). A generalized estimating equations model will be used to examine the group, time, and interaction (Time × Group) effect of the interventions on the outcome assessments (intention-to-treat analysis) across the three time points, and to compute a within-group comparison of objective physiological parameters and adherence to the assigned interventions in the experimental group.

Conclusions

The innovative, three-level mobile HAPPY app will promote beneficial behavioral strategies to alleviate post-pandemic physiological and psychosocial distress.

Trial registration

ClinicalTrials.gov, NCT05459896. Registered on 15 July 2022.

Stabbed by motorcycle? Reconstruction of an unusual traffic accident

The reconstruction of traffic accidents involving powered two-wheelers (PTWs) frequently proves to be a challenging task. A case in which a fatal head-on crash of a PTW with a small truck where only minor vehicles damage was observed but resulted in isolated fatal chest trauma is discussed here. External examination of the corpse revealed two lacerations on the back, at the first glance implying sharp trauma. Based on the accident traces, the technical expert assumed an emergency break of the PTW rider resulting in a rotation of the PTW in terms of a wheelie on the front wheel. The first contact between the PTW rider and the tail end of the small truck probably occurred with the upper side of the helmet, and then, the back handle of the PTW caused the stab-like injuries followed by compression of the rider between the small truck or asphalt and the PTW. Based on the few accident traces available, neither a reconstruction of the pre-impact velocity nor a detailed reconstruction of the PTW rider kinematics was possible. However, using an interdisciplinary approach, the principal collision position as well as the injury mechanisms could be reconstructed.

Accuracy Verification of Surface Models of Architectural Objects from the iPad LiDAR in the Context of Photogrammetry Methods

The creation of accurate three-dimensional models has been radically simplified in recent years by developing photogrammetric methods. However, the photogrammetric procedure requires complex data processing and does not provide an immediate 3D model, so its use during field (in situ) surveys is infeasible. This paper presents the mapping of fragments of built structures at different scales (finest detail, garden sculpture, architectural interior, building facade) by using a LiDAR sensor from the Apple iPad Pro mobile device. The resulting iPad LiDAR and photogrammetric models were compared with reference models derived from laser scanning and point measurements. For small objects with complex geometries acquired by iPad LiDAR, up to 50% of points were unaligned with the reference models, which is much more than for photogrammetric models. This was primarily due to much less frequent sampling and, consequently, a sparser grid. This simplification of object surfaces is highly beneficial in the case of walls and building facades as it smooths out their surfaces. The application potential of the IPad LiDAR Pro is severely constrained by its range cap being 5 m, which greatly limits the size of objects that can be recorded, and excludes most buildings.

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones' TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build a 3D point cloud. In particular, the smartphones were tested in several case studies involving the scanning of several objects: 10 building material samples, a statue, an interior room environment and the remains of a Doric column in a major archaeological site. The quality of the point clouds was evaluated through visual analysis and using three eigenfeatures: surface variation, planarity and omnivariance. Based on this approach, some issues with the point clouds generated by smartphones were highlighted, such as surface splitting, loss of planarity and inertial navigation system drift problems. In addition, it can finally be deduced that, in the absence of scanning problems, the accuracies achievable from this type of scanning are ~1–3 cm. Therefore, this research intends to describe a method of quantifying anomalies occurring in smartphone scans and, more generally, to verify the quality of the point cloud obtained with these devices.

Navigation of frameless fixation for gamma knife radiosurgery using fixed augmented reality

Augmented reality (AR) offers a new medical treatment approach. We aimed to evaluate frameless (mask) fixation navigation using a 3D-printed patient model with fixed-AR technology for gamma knife radiosurgery (GKRS). Fixed-AR navigation was developed using the inside-out method with visual inertial odometry algorithms, and the flexible Quick Response marker was created for object-feature recognition. Virtual 3D-patient models for AR-rendering were created via 3D-scanning utilizing TrueDepth and cone-beam computed tomography (CBCT) to generate a new GammaKnife Icon™ model. A 3D-printed patient model included fiducial markers, and virtual 3D-patient models were used to validate registration accuracy. Registration accuracy between initial frameless fixation and re-fixation navigated fixed-AR was validated through visualization and quantitative method. The quantitative method was validated through set-up errors, fiducial marker coordinates, and high-definition motion management (HDMM) values. A 3D-printed model and virtual models were correctly overlapped under frameless fixation. Virtual models from both 3D-scanning and CBCT were enough to tolerate the navigated frameless re-fixation. Although the CBCT virtual model consistently delivered more accurate results, 3D-scanning was sufficient. Frameless re-fixation accuracy navigated in virtual models had mean set-up errors within 1 mm and 1.5° in all axes. Mean fiducial marker differences from coordinates in virtual models were within 2.5 mm in all axes, and mean 3D errors were within 3 mm. Mean HDMM difference values in virtual models were within 1.5 mm of initial HDMM values. The variability from navigation fixed-AR is enough to consider repositioning frameless fixation without CBCT scanning for treating patients fractionated with large multiple metastases lesions (> 3 cm) who have difficulty enduring long beam-on time. This system could be applied to novel GKRS navigation for frameless fixation with reduced preparation time.

Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

Repurposing an EMG Biofeedback Device for Gait Rehabilitation: Development, Validity and Reliability

Gait impairment often limits physical activity and negatively impacts quality of life. EMG-Biofeedback (EMG-BFB), one of the more effective interventions for improving gait impairment, has been limited to laboratory use due to system costs and technical requirements, and has therefore not been tested on a larger scale. In our research, we aimed to develop and validate a cost-effective, commercially available EMG-BFB device for home- and community-based use. We began by repurposing mTrigger® (mTrigger LLC, Newark, DE, USA), a cost-effective, portable EMG-BFB device, for gait application. This included developing features in the cellphone app such as step feedback, success rate, muscle activity calibration, and cloud integration. Next, we tested the validity and reliability of the mTrigger device in healthy adults by comparing it to a laboratory-grade EMG system. While wearing both devices, 32 adults walked overground and on a treadmill at four speeds (0.3, 0.6, 0.9, and 1.2 m/s). Statistical analysis revealed good to excellent test-retest reliability (r > 0.89) and good to excellent agreement in the detection of steps (ICC > 0.85) at all speeds between two systems for treadmill walking. Our results indicated that mTrigger compared favorably to a laboratory-grade EMG system in the ability to assess muscular activity and to provide biofeedback during walking in healthy adults.

Fabrication of 3D Models for Microtia Reconstruction Using Smartphone-Based Technology

OBJECTIVE

Microtia reconstruction is technically challenging due to the intricate contours of the ear. It is common practice to use a two-dimensional tracing of the patient's normal ear as a template for the reconstruction of the affected side. Recent advances in three-dimensional (3D) surface scanning and printing have expanded the ability to create surgical models preoperatively. This study aims to describe a simple and affordable process to fabricate patient-specific 3D ear models for use in the operating room.

STUDY DESIGN

Applied basic research on a novel 3D optical scanning and fabrication pathway for microtia reconstruction.

SETTING

Tertiary care university hospital.

METHODS

Optical surface scanning of the patient's normal ear was completed using a smartphone with facial recognition capability. The Heges application used the phone's camera to capture the 3D image. The 3D model was digitally isolated and mirrored using the Meshmixer software and printed with a 3D printer (Monoprice^TM Select Mini V2) using polylactic acid filaments.

RESULTS

The 3D model of the ear served as a helpful intraoperative reference and an adjunct to the traditional 2D template. Collectively, time for imaging acquisition, editing, and fabrication was approximately 3.5 hours. The upfront cost was around $210, and the recurring cost was approximately $0.35 per ear model.

CONCLUSION

A novel, low-cost approach to fabricate customized 3D models of the ear is introduced. It is feasible to create individualized 3D models using currently available consumer technology. The low barrier to entry raises the possibility for clinicians to incorporate 3D printing into various clinical applications.