Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.

A novel activation function based on DNA enzyme-free hybridization reaction and its implementation on nonlinear molecular learning systems.

With the advent of the post-Moore's Law era, the development of traditional silicon-based computers has reached its limit, and there is an urgent need to develop new computing technologies to meet the needs of science, technology, and daily life. Due to its super-strong parallel computing capability and outstanding data storage capacity, DNA computing has become an important branch and hot research topic of new computer technology. DNA enzyme-free hybridization reaction technology is widely used in DNA computing, showing excellent performance in computing power and information processing. Studies have shown that DNA molecules not only have the computing function of electronic devices, but also exhibit certain human brain-like functions. In the field of artificial intelligence, activation functions play an important role as they enable artificial intelligence systems to fit and predict non-linear and complex variable relationships. Due to the difficulty of implementing activation functions in DNA computing, DNA circuits cannot easily achieve all the functions of artificial intelligence. DNA circuits need to rely on electronic computers to complete the training and learning process. Based on the parallel computing characteristics of DNA computing and the kinetic features of DNA molecule displacement reactions, this paper proposes a new activation function. This activation function can not only be easily implemented by DNA enzyme-free hybridization reaction reactions, but also has good nesting properties in DNA circuits, and can be cascaded with other DNA reactions to form a complete DNA circuit. This paper not only provides the mathematical analysis of the proposed activation function, but also provides a detailed analysis of its kinetic features. The activation function is then nested into a nonlinear neural network for DNA computing. This system is capable of fitting and predicting a certain nonlinear function.

Replacing the Scalpel With a Computer Mouse: An Evaluation of Time Spent on Electronic Health Record for Plastic Surgery Residents and Its Impact on Resident Training.

BACKGROUND: Following the integration of the electronic health record (EHR) into the healthcare system, concern has grown regarding EHR use on physician well-being. For surgical residents, time spent on the EHR increases the burden of a demanding, hourly restricted schedule and detracts from time spent honing surgical skills. To better characterize these burdens, we sought to describe EHR utilization patterns for plastic surgery residents. METHODS: Integrated plastic surgery resident EHR utilization from March 2019 to March 2020 was extracted via Cerner Analytics at a tertiary academic medical center. Time spent in the EHR on-duty (0600-1759) and off-duty (1800-0559) in the form of chart review, orders, documentation, and patient discovery was analyzed. Statistical analysis was performed in the form of independent t tests and Analysis of Variance (ANOVA). RESULTS: Twelve plastic surgery residents spent a daily average of 94 ± 84 minutes on the EHR, one-third of which was spent off-duty. Juniors (postgraduate years 1-3) spent 123 ± 99 minutes versus seniors (postgraduate years 4-6) who spent 61 ± 49 minutes (P < 0.01). Seniors spent 19% of time on the EHR off-duty, compared with 37% for juniors (P < 0.01). Chart review comprised the majority (42%) of EHR usage, followed by patient discovery (22%), orders (14%), documentation (12%), other (6%), and messaging (1%). Seniors spent more time on patient discovery (25% vs 21%, P < 0.001), while juniors spent more time performing chart review (48% vs 36%, P = 0.19). CONCLUSION: Integrated plastic surgery residents average 1.5 hours on the EHR daily. Junior residents spend 1 hour more per day on the EHR, including more time off-duty and more time performing chart review. These added hours may play a role in duty hour violations and detract from obtaining operative skill sets.

Optimizing FPGA implementation of high-precision chaotic systems for improved performance.

Developing chaotic systems-on-a-chip is gaining much attention due to its great potential in securing communication, encrypting data, generating random numbers, and more. The digital implementation of chaotic systems strives to achieve high performance in terms of time, speed, complexity, and precision. In this paper, the focus is on developing high-speed Field Programmable Gate Array (FPGA) cores for chaotic systems, exemplified by the Lorenz system. The developed cores correspond to numerical integration techniques that can extend to the equations of the sixth order and at high precision. The investigation comprises a thorough analysis and evaluation of the developed cores according to the algorithm complexity and the achieved precision, hardware area, throughput, power consumption, and maximum operational frequency. Validations are done through simulations and careful comparisons with outstanding closely related work from the recent literature. The results affirm the successful creation of highly efficient sixth-order Lorenz discretizations, achieving a high throughput of 3.39 Gbps with a precision of 16 bits. Additionally, an outstanding throughput of 21.17 Gbps was achieved for the first-order implementation coupled with a high precision of 64 bits. These outcomes set our work as a benchmark for high-performance characteristics, surpassing similar investigations reported in the literature.

The persistent educational digital divide and its impact on societal inequality.

Computers and the Internet are widely recognized as fundamental to academic and future success on both the individual and the societal level. Moreover, the academic success of school-age children is now increasingly tied to access to educational technology, a reality that became even more apparent during the pandemic. While academic performance is viewed as the major outcome of using educational technology, this study looks at a crucial early stage in the educational technology value chain, specifically; 1) to what extent do students use computers and the Internet in their homes and at school and 2) what is the extent and nature of disparities in student access to educational technology. This study was conducted using the national CPS 2019 Computer and Internet Use Survey of 23,064 school age children. We used bivariate tables and multivariate logistic regression analysis to analyze the data. Results indicate that substantial disparities in the use of educational technology exist in the U.S. Overall, 28.0% of school age children reported they did not use the Internet at school or at home and another 22.8% reported using the Internet at home but not at school. Significantly, individual and community demographic characteristics and household and school technology resources contribute to these disparities. It is clear that if fundamental educational technology and the resources needed to effectively achieve academic success are unavailable in the home, then they must be provided in schools. Without educational technology and resources, the societal value added through growing use of this technology will not materialize for our students. We conclude that committing to increasing educational technology resources in the schools will have multiple future societal benefits and improve the effectiveness of the educational technology value chain.

MaRGE: A graphical environment for MaRCoS.

The open-source console MaRCoS, which stands for "Magnetic Resonance Control System", combines hardware, firmware and software elements for integral control of Magnetic Resonance Imaging (MRI) scanners. Previous developments have focused on making the system robust and reliable, rather than on users, who have been somewhat overlooked. This work describes a Graphical User Interface (GUI) designed for intuitive control of MaRCoS, as well as compatibility with clinical environments. The GUI is based on an arrangement of tabs and a renewed Application Program Interface (API). Compared to the previous versions, the MaRGE package ("MaRCoS Graphical Environment") includes new functionalities such as the possibility to export images to standard DICOM formats, create and manage clinical protocols, or display and process image reconstructions, among other features conceived to simplify the operation of MRI scanners. All prototypes in our facilities are commanded by MaRCoS and operated with the new GUI. Here we report on its performance on an experimental 0.2 T scanner designed for hard-tissue, as well as a 72 mT portable scanner presently installed in the radiology department of a large hospital. The possibility to customize, adapt and streamline processes has substantially improved our workflows and overall experience.

Extracorporeal shock wave lithotripsy: retrospective study on possible predictors of treatment success and revisiting the role of non-contrast-enhanced computer tomography in kidney and ureteral stone disease.

Extracorporeal shock wave lithotripsy (ESWL) is a safe and efficient treatment option for urinary stone disease. The overall stone-free rate (SFR) varies significantly. This study aimed to assess the influence of stone size, location, stone density, and skin-to-stone distance (SSD), on the outcome of ESWL. We assessed whether pre-treatment non-contrast-enhanced CT scan (NCCT) confers significant advantages compared to kidney-ureter-bladder film (KUB) only. We reviewed the medical records of 307 cases (165 men, 142 women) with renal and ureteral stones treated consecutively at our institution with ESWL between 2020 and 2023. 44 of these underwent a NCCT. The outcome of ESWL was defined in two ways: visible stone fragmentation on KUB, and the need for further treatment. Overall success of fragmentation was 85% (261 patients). 61% of patients (n = 184) didn't need any further treatment. Stone size and location correlated significantly with treatment outcomes regarding the need for further treatment (p = 0.004) and stone fragmentation (p = 0.016), respectively. Unlike mean SSD (p = 0.462), the mean attenuation value (MAV) significantly correlated with the need for retreatment (p = 0.016). MAV seems to be a better predictor of treatment success (AUC of the ROC curve: 0.729), compared to stone size (AUC: 0.613). The difference between groups (with and without NCCT) in both treatment outcomes did not reach statistical significance. During decision-making, information regarding SSD and MAV can be useful in more dubious scenarios. However, it appears that their inclusion doesn't provide substantial advantages when compared to relying solely on KUB.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Computer-aided cognitive training combined with tDCS can improve post-stroke cognitive impairment and cerebral vasomotor function: a randomized controlled trial.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is the focus and difficulty of poststroke rehabilitation intervention with an incidence of up to 61%, which may be related to the deterioration of cerebrovascular function. Computer-aided cognitive training (CACT) can improve cognitive function through scientific training targeting activated brain regions, becoming a popular training method in recent years. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique, can regulate the cerebral vascular nerve function, and has an effect on the rehabilitation of cognitive dysfunction after stroke. This study examined the effectiveness of both CACT and tDCS on cognitive and cerebrovascular function after stroke, and explored whether CACT combined with tDCS was more effective. METHODS: A total of 72 patients with PSCI were randomly divided into the conventional cognitive training (CCT) group (n = 18), tDCS group (n = 18), CACT group (n = 18), and CACT combined with tDCS group (n = 18). Patients in each group received corresponding 20-minute treatment 15 times a week for 3 consecutive weeks. Montreal Cognitive Assessment (MoCA) and the Instrumental Activities of Daily Living Scale (IADL) were used to assess patients' cognitive function and the activities of daily living ability. Transcranial Doppler ultrasound (TCD) was used to assess cerebrovascular function, including cerebral blood flow velocity (CBFV), pulse index (PI), and breath holding index (BHI). These outcome measures were measured before and after treatment. RESULTS: Compared with those at baseline, both the MoCA and IADL scores significantly increased after treatment (P < 0.01) in each group. There was no significantly difference in efficacy among CCT, CACT and tDCS groups. The CACT combined with tDCS group showed greater improvement in MoCA scores compared with the other three groups (P < 0.05), especially in the terms of visuospatial and executive. BHI significantly improved only in CACT combined with tDCS group after treatment (p ≤ 0.05) but not in the other groups. Besides, no significant difference in CBFV or PI was found before and after the treatments in all groups. CONCLUSION: Both CACT and tDCS could be used as an alternative to CCT therapy to improve cognitive function and activities of daily living ability after stroke. CACT combined with tDCS may be more effective improving cognitive function and activities of daily living ability in PSCI patients, especially visuospatial and executive abilities, which may be related to improved cerebral vasomotor function reflected by the BHI. TRIAL REGISTRATION NUMBER: The study was registered in the Chinese Registry of Clinical Trials (ChiCTR2100054063). Registration date: 12/08/2021.

Comparison of condylar position after free fibular flap mandibular reconstruction using computer-assisted and traditional techniques.

OBJECTIVES: To compare the changes in condylar position after mandibular reconstruction with free fibular flap(FFF) and the differences between computer-assisted techniques and traditional methods on CT images. METHODS: Thirty-four patients who underwent mandibular reconstruction with free fibular flap were selected according to the inclusion and exclusion criteria. In the 3D group, virtual surgical planning (VSP) with osteotomy cutting plate and placement guiding plate were used, while the traditional group underwent freehand reconstruction. The CT data of 68 temporomandibular joints (TMJs) were recorded before and immediately after surgery. The condylar position was evaluated by measuring the anterior space (AS), posterior space (PS) and superior space (SS), and the ln (PS/AS) was calculated according to the method proposed by Pullinger and Hollender. RESULTS: In the patients included in the 3D group, the condyle on the ipsilateral side moved slightly backward; however, in the patients in the traditional group, the ipsilateral side moved considerably anteroinferior. No obvious changes on the contralateral side were noted. In the 3D group, 33% of ipsilateral condyles were in the posterior position postoperatively when compared with the preoperative position (13%). In the traditional group, the number of ipsilateral condyles in the anterior position increased from 4 to 10, accounting for 53% postoperatively. Contrary to the traditional group, the 3D group presented less condylar displacement on the ipsilateral side postoperatively. CONCLUSIONS: This study showed a decreased percentage of change in condylar position postoperatively when VSP was used. Virtual surgical planning improved the accuracy of FFF mandibular reconstruction and made the condylar position more stable.

Neuromorphic one-shot learning utilizing a phase-transition material.

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan.

Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.

A novel algorithm for maximum power point tracking using computer vision (CVMPPT).

The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

A model study of teaching method reform of computer laboratory course integrating internet of things technology.

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

Efficacy of computer-assisted robotic based clinical training program for spinal oncology education on pedicle screw placement.

Pedicle screw placement (PSP) is the fundamental surgical technique that requires high accuracy for novice orthopedists studying spinal oncology education. Therefore, we set forth to establish a computer-assisted robotic navigation training program for novice spinal oncology education. Novice orthopedists were involved in this study to evaluate the feasibility and safety of the computer-assisted robotic navigation (CARN) training program. In this research, trainees were randomly taught by the CARN training program and the traditional training program. We prospectively collected the clinical data of patients with spinal tumors from 1st May 2021 to 1st March 2022. The ability of PSP was evaluated by cumulative sum (CUSUM) analysis, learning curve, and accuracy of pedicle screws. The patients included in both groups had similar baseline characteristics. In the CUSUM analysis of the learning curve for accurate PSP, the turning point in the CARN group was lower than that in the traditional group (70th vs. 92nd pedicle screw). The LC-CUSUM test indicated competency for PSP at the 121st pedicle screw in the CARN group and the 138th pedicle screw in the traditional group. The accuracy of PSP was also significantly higher in the CARN group than in the traditional group (88.17% and 79.55%, P = 0.03 < 0.05). Furthermore, no major complications occurred in either group. We first described CARN in spinal oncology education and indicated the CARN training program as a novel, efficient and safe training program for surgeons.

Advanced disk herniation computer aided diagnosis system.

Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to boost the performance of such algorithms even further, which include various transfer learning techniques, data augmentation, and feature concatenation. Normally, the use of these advanced techniques highly depends on the size and nature of the dataset being used. In the case of fine-grained medical image sets, which have subcategories within the main categories in the image set, there is a need to find the combination of the techniques that work the best on these types of images. In this work, we utilize these advanced techniques to find the best combinations to build a state-of-the-art lumber disc herniation computer-aided diagnosis system. We have evaluated the system extensively and the results show that the diagnosis system achieves an accuracy of 98% when it is compared with human diagnosis.

Predicting mechanical neck pain intensity in computer professionals using machine learning: identification and correlation of key features.

Introduction: Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method: This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results: The "NDI Score (%)" consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion: Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.

Perspectives of Patients and Professionals on Implementing a Computer Adaptive Vision-Related Quality of Life Outcome (CAT-EyeQ) in Clinical Practice.

Purpose: The CAT-EyeQ is a computer adaptive test (CAT) which measures vision-related quality of life in patients having exudative retinal diseases. The aim of this study is to investigate the usability of the CAT-EyeQ in clinical practice and identify potential barriers and facilitators for implementation (problem analysis). Methods: Patients and health care professionals participated in the study regarding the usability of the CAT-EyeQ, and clinic managers and health care professionals were included in the problem analysis for implementation. In total, we conducted 18 semi-structured interviews. The Consolidated Framework for Implementation Research (CFIR) was used to develop the interview guides and to structure results. Results: Six themes were derived from the usability study and problem analysis: (1) quality of the CAT-EyeQ and the applicability to patients' needs and preferences, (2) embedding the CAT-EyeQ in current practice, (3) implementation climate of the eye hospitals, (4) attitude of professionals, (5) engaging and encouraging professionals, and (6) integration of the CAT-EyeQ in health care - needs after piloting. Conclusions: Patients and professionals mentioned that the CAT-EyeQ improved insight into the impact of eye diseases on a patient's daily life, it allowed for more attention on the patient perspective and the structured measurement of vision-related quality of life. The main perceived barriers mentioned by professionals for using the CAT-EyeQ were lack of time and the integration of the patient-reported outcome measure (PROM) results within the electronic patient record (EPR). Translational Relevance: The CAT-EyeQ, accompanied by an overview of stakeholder perspectives resulting from this implementation study, can now be used in clinical practice.

Accuracy of implant placement with computer-aided static, dynamic, and robot-assisted surgery: a systematic review and meta-analysis of clinical trials.

This systematic review explores the accuracy of computerized guided implant placement including computer-aided static, dynamic, and robot-assisted surgery. An electronic search up to February 28, 2023, was conducted using the PubMed, Embase, and Scopus databases using the search terms "surgery", "computer-assisted", "dynamic computer-assisted", "robotic surgical procedures", and "dental implants". The outcome variables were discrepancies including the implant's 3D-coronal, -apical and -angular deviations. Articles were selectively retrieved according to the inclusion and exclusion criteria, and the data were quantitatively meta-analysed to verify the study outcomes. Sixty-seven articles were finally identified and included for analysis. The accuracy comparison revealed an overall mean deviation at the entry point of 1.11 mm (95% CI: 1.02-1.19), and 1.40 mm (95% CI: 1.31-1.49) at the apex, and the angulation was 3.51˚ (95% CI: 3.27-3.75). Amongst computerized guided implant placements, the robotic system tended to show the lowest deviation (0.81 mm in coronal deviation, 0.77 mm in apical deviation, and 1.71˚ in angular deviation). No significant differences were found between the arch type and flap operation in cases of dynamic navigation. The fully-guided protocol demonstrated a significantly higher level of accuracy compared to the pilot-guided protocol, but did not show any significant difference when compared to the partially guided protocol. The use of computerized technology clinically affirms that operators can accurately place implants in three directions. Several studies agree that a fully guided protocol is the gold standard in clinical practice.