Objective Assessment Research Articles

Development and validation of a virtual teaching method for minimally invasive surgery skills: a prospective cohort study.

The COVID-19 pandemic led to a significant reduction in operative exposure for surgical trainees, necessitating alternative training methods to mitigate the impact on surgical education. This study sought to evaluate whether minimally invasive surgery (MIS) skills could be taught remotely using widely available technology with objective assessments of proficiency. This was a pilot observational study with comparative assessment of face-to-face (F2F) and virtual training of novice learners in MIS skills. Performance and objective cognitive workload parameters (Surgical Task Load Index (SURG-TLX) score, heart rate and pupil metrics) were evaluated. The assessments were peg transfer (McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS)) and suturing (Suturing Training and Testing (SUTT)) tasks performed using box trainers. Virtual teaching was conducted by expert trainers using a web-based streaming platform. Technical challenges of delivering a virtual MIS skills course were addressed after a pilot course. Participants (n = 20) in the final course had similar baseline characteristics and were randomly allocated to F2F (n = 8) and virtual (n = 12) teaching groups. Participants in the online group completed the peg transfer task faster than the F2F group (11.25 minutes vs. 16.88 minutes; P = 0.015). There were no significant differences in all other MISTELS and SUTT performance measures between groups. Cognitive workload parameters (SURG-TLX score, heart rate and pupil metrics) were also similar between groups. This study has demonstrated that virtual teaching of MIS skills using a web-based streaming platform is feasible and effective, providing the foundation for low-cost, effective, and scalable MIS skills programs in the future.

Investigation of bi-directional relations between pre-sleep electronic media use and sleep: A seven-day dairy study

The current study aimed to examine the bi-directional relationships between pre-sleep electronic media use and sleep, and to assess the relative predictive strength of different types of electronic media use in the evening on subsequent sleep. Eighty-eight young adults (20.57 ± 0.99, 27 males) completed an online intake survey and received notifications to complete daily diaries for seven consecutive days. Results of dynamic structural equation models (DSEM) revealed that the total screen time, sleep onset time, sleep latency, sleep duration, and sleep quality showed significant positive autoregressive parameters. In contrast, the results revealed no statistically significant cross-lagged associations between the total screen time in the evening and sleep outcomes. Inspection of the predictive strength of various types of screen activities at the within-person level revealed that increased active social media use is linked to delayed sleep onset, while internet browsing is associated with shorter sleep latency. At the between-person level, screen time of T.V. watching, video streaming, social media use, and shopping correlated with delayed sleep onset. Shopping was also connected to longer sleep latency. Additionally, more time spent on T.V., video streaming, non-work internet browsing, and shopping was linked to reduced total sleep duration. These findings indicated a screen activity-dependent association between pre-sleep screen time and subsequent sleep. More objective assessments of electronic media use (e.g., app recording screen time) and sleep (e.g., actigraphy) and longitudinal monitoring over a relatively long period (i.e., several weeks or months) are warred to further elucidate (cross-lagged) relationships between electronic media use and sleep.

Application of objective optical quality analysis system in guiding early posterior capsule opacification laser treatment: An observational study.

In the present study, we aimed to demonstrate the objective guiding role of objective scatter index (OSI) in patients with early stage posterior capsule opacification (PCO), especially in patients with multifocal intraocular lens (IOL) implantation. A prospective randomized case analysis was conducted on elderly patients experiencing PCO, exhibiting best-corrected visual acuity (BCVA) below 0.2 LogMAR. Exclusion criteria comprised patients with diabetes, glaucoma, high myopia, trauma, endothelial dystrophy, maculopathy or prior eye surgery affecting visual acuity. The main observational indicators were patient gender, age, post-cataract surgery duration, BCVA (LogMAR), and OSI values. For patients undergoing laser posterior capsulotomy, meticulous documentation of pre-laser OSI values and post-laser OSI values was undertaken. Subjective visual quality was then compared using the Chinese version visual function index-14 (VF-14-CN). The study comprised 65 eyes from 56 patients. PCO developed, on average, 2.06 ± 0.94 years (ranging from 0.5 to 4 years) after the initial surgery, with the OSI consistently exceeding 3.0. Among these cases, only 40 eyes underwent laser posterior capsulotomy using Nd:YAG laser, including 26 eyes implanted with monofocal IOLs and 14 eyes implanted with multifocal IOLs. Following laser posterior capsulotomy for early-stage secondary cataracts, there was a significant decrease in OSI values compared to the presurgery state, coupled with a notable increase in VF-14-CN scores. Among the 14 cases with multifocal IOL implantation, pre-laser VF-14-CN scores were lower compared to those with monofocal IOLs. Post-laser posterior capsulotomy, patients reported a marked improvement in self-perceived symptoms. The OQAS functioned as an objective assessment metric for the laser treatment of early stage PCO. In cases involving multifocal IOL implantation, the visual disturbance caused by PCO was more pronounced, underscoring the significance of OSI as a valuable guide for laser treatment in these patients.

Multivariable model for gait pattern differentiation in elderly patients with hip and knee osteoarthritis: A wearable sensor approach

BackgroundHip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data. MethodParticipants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features. FindingsWe included 57 knee OA patients (30 females, median age 68 [range 49–89], median BMI 29.7 [range 21.0–45.9]) and 42 hip OA patients (19 females, median age 70 [range 47–89], median BMI 28.3 [range 20.4–37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively. InterpretationThe study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

Validation of a Low-Cost Manometer to Assess of Tongue, Lip, Cheek, and Respiratory Strength: A Laboratory-Based Study.

The objective of this study was to characterize the level of agreement between three manometers: (1) Iowa Oral Performance Instrument (IOPI)-the reference standard for tongue, lip, and cheek strength assessments; (2) MicroRPM Respiratory Pressure Meter (MicroRPM)-the reference standard for respiratory strength assessments; and (3) Digital Pressure Manometer (DPM)-an alternative, low-cost pressure testing manometer. Manual pressures were simultaneously applied to the IOPI and DPM, and to the MicroRPM and DPM, within a controlled laboratory setting. Agreement in pressure readings were analyzed using descriptive statistics, Lin's concordance correlation, and Bland-Altman Plots. Agreement was interpreted as "poor" if ρc < 0.90, "moderate" if ρc = 0.90 - < 0.95, "substantial" if ρc = 0.95 - < 0.99, and "excellent" if ρc ≥ 0.99. Differences in pressure readings between the DPM and clinical reference standards were consistently present yet highly predictable. There was a median absolute difference of 2.0-3.9 kPa between the IOPI and DPM, and 4.5-9.8 cm H2O between the MicroRPM and DPM. Lin's concordance revealed "substantial" agreement between the IOPI and DPM (ρc = 0.98) and the MicroRPM and DPM (ρc = 0.99). The DPM revealed higher pressure readings when compared to the IOPI and MicroRPM. However, differences in pressure readings were relatively small, highly predictable, and yielded substantial overall agreement. These findings suggest the DPM may be a valid, lower-cost alternative for objective assessments of tongue, lip, cheek, and respiratory muscle strength. Future research should expand on the present findings in clinical patient populations. NA Laryngoscope, 2024.

Computer Vision for Assessing Surgical Movements in Neurosurgery.

Objective evaluation of microsurgical technique quality is vital for successful training in neurosurgery. This study aimed to assess the accuracy of automatically detecting a neurosurgeon's proper posture and hand positioning using computer vision. We employed the RTMPose neural network model to identify key anatomical points in the neurosurgeon's projection and calculated various angles formed by connecting these points. By utilizing machine learning on these angles, we were able to classify images of the surgeon's posture and hands into correct positions and various types of errors with an accuracy of at least 0.9. Computer vision enables successful detection and objective assessment of the neurosurgeon's posture and hand positions. The high accuracy of this detection can pave the way for a new training approach in neurosurgery.

Refining Environmental Sustainability Governance Reports through Fuzzy Systems Evaluation and Scoring

Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and ratings disclosed in these reports. Moreover, there is a need for more objective methods to determine the weight distribution of indicator items. This study introduces a novel approach utilizing semantic variables in fuzzy theory and a multiple logic fuzzy inference system to develop an ESG environmental management performance assessment model. Therefore, this paper aims to develop a novel approach utilizing semantic variables and a multiple logic fuzzy inference system to quantitatively evaluate the sustainable performance of an environmental management plan. This research also aims to ensure fair and objective assessment outcomes, providing valuable guidance for enterprises in implementing performance management strategies. Key aspects investigated include the impact of membership functions, the extended utilization of semantic variables and logical rules, a comparative analysis of traditional weight assessments, and the limitations of applying fuzzy theory. Through comprehensive discussions and calculations, it is evident that fuzzy theory offers considerable flexibility in application. By tailoring fuzzy rules and selecting appropriate membership functions, diverse application scenarios can be accommodated. The Fuzzy systems evaluation and scoring EMP model generates EMP evaluation scores ranging from 1.76 to 8.29 for Gaussian membership, 1.80 to 8.19 for Triangular membership-A, 1.92 to 8.00 for Triangular membership-B, and 1.81 to 8.19 for Quadrilateral trapezoidal membership, based on simulated rating scenarios using the semantic variables of completeness and feasibility. This approach successfully incorporates distribution logic from subjective membership degrees to evaluate EMP scores. The findings demonstrate that fuzzy theory enables the consideration of multiple factors and facilitates the provision of objective-level membership, underscoring its potential in addressing complex evaluation challenges. This study illuminates the versatility of the fuzzy system theory, with its applications poised to extend across various domains.

Comparison of the effect of clear twin block and traditional twin block on speech: a randomized clinical trial.

To compare the effect of clear twin block (CTB) and traditional twin block (TTB) appliances on speech. In this randomized clinical trial, 18 skeletal Class II (Class II, division 1) growing patients were selected and randomly divided into CTB and TTB groups. Objective and semiobjective speech assessment tests were performed for vowel and consonant analyses at four time intervals: before (T0), immediately after (T1), 1 month after (T2), and 3 months after (T3) inserting the appliance. Data were analyzed using analysis of variance and independent t-test at the .05 significance level. Intergroup comparisons showed that the CTB group had less speech distortion immediately after insertion of the appliance (P < .05) than the TTB group; however, the differences were not significant at other time intervals. Intragroup comparisons showed that the number of distortions decreased significantly from T1 to T3 in both groups (P < .05). In contrast to CTB, the T0-T3 comparison was significant in the TTB group. Although both appliances had some effects on speech, CTB had less speech distortion immediately after insertion, which is a very crucial moment in patient compliance. Additionally, articulation structures adapted to CTB faster than to TTB.

How we measure hypoglossal nerve stimulator outcome matters: titration versus single amplitude efficacy sleep studies.

Hypoglossal nerve stimulation (HGNS) is a common treatment for obstructive sleep apnea (OSA). Objective assessment of HGNS efficacy measures apnea-hypopnea index (AHI) by multi-amplitude titration polysomnography (tPSG) and/or a single amplitude efficacy sleep study (eHST). Both tests have been used to determine efficacy despite significantly different protocols. This project's aim was to determine differences in objective outcomes in HGNS patients who underwent both tPSG and eHST post-operatively. Data from 379 consecutive HGNS patients were retrospectively reviewed. Inclusion requirements were a pre-operative sleep study, a post-operative tPSG, and then an eHST, which at our institution is a type 3 home study. AHI mean and differences were calculated. Wilcoxon rank sum tests were used to analyze differences between tPSG and eHST. Sher15 criteria (post-operative AHI≤15 events/hour and ≥50% reduction from baseline) was calculated and compared by χ2 tests. Ultimately 61 patients met inclusion criteria with an average pre-operative AHI=33.2. When comparing the subject's tPSG versus eHST, tPSG AHI was significantly lower (AHI=8.8 versus AHI=17.6; respectively, p<0.001). There was also a difference in the percentage of patients that met Sher15 criteria when using tPSG (80.3%) versus eHST AHI (45.9%). HGNS patient's postoperative tPSG AHI was significantly lower than their eHST outcome. This work highlights the importance of reporting the type of post-operative study used in evaluating HGNS efficacy and the need for single amplitude, full-night studies to assess HGNS efficacy more accurately.

Clinical audit of an artificial intelligence (AI) empowered smile simulation system: a prospective clinical trial

Smile aesthetics is an important factor to consider during orthodontic treatment planning. The aim of the present study is to assess the predictability of Invisalign SmileView for digital AI smile simulation in comparison to actual smile treatment outcomes, using various smile assessment parameters. A total of 24 adult subjects (12 females and 12 males; mean age 22 ± 5.2 years) who chose to be treated using Invisalign were prospectively recruited to have their pretreatment smiles captured using the Invisalign SmileView to simulate their new smiles before treatment was started. Patients were then treated using upper and lower Invisalign aligners with average treatment time of 18 ± 6 months. Full post-treatment records were obtained and full smile frame images of simulated smile and actual final smile of each subject were evaluated by an independent examiner using an objective assessment sheet. Ten smile variants were used to assess the characteristics of the full smile images. Significance level was set at P < 0.05. The ICC for the quantitative parameters showed that there was an overall excellent & good internal consistency (alpha value > 0.7 & > 0.9). The Independent t test was performed amongst the quantitative variables. The P value was not significant for all except maxillary inter canine width (P = 0.05), stating that for the five variables namely; philtrum height, commissure height, smile width, buccal corridor and smile index, actual mean values were similar to the simulation mean values. For the qualitative variables, the Kappa value ranged between 0.66 and − 0.75 which showed a substantial level of agreement between the examiners. Additionally, the Chi square test for the qualitative variables, revealed that the P value was found to be significant in all except lip line. This implies that only the lip line values are comparable. More optimal lip lines, straighter smile arcs and more ideal tooth display were achieved in actual post treatment results in comparison to the initially predicted smiles. Five quantitative smile assessment parameters i.e., philtrum height, commissure height, smile width, buccal corridor, and smile index, could be used as reliable predictors of smile simulation. Maxillary inter canine width cannot be considered to be a reliable parameter for smile simulation prediction. A single qualitative parameter, namely the lip line, can be used as a reliable predictor for smile simulation. Three qualitative parameters i.e., most posterior tooth display, smile arc, and amount of lower incisor exposure cannot be considered as reliable parameters for smile prediction.Trial Registration number and date: NCT06123585, (09/11/2023)

Objective assessment of the effect of surgery on limb function after medial femoral condyle free flap harvest: biomechanical parameters

The aim of this study was to evaluate the influence of medial femoral condyle (MFC) free flap harvest on donor site muscle strength and kinematic parameters of gait. The study included 30 patients treated for head and neck squamous cell carcinoma who underwent reconstruction with an MFC free flap. In each case, the donor site was the left thigh. A dynamometer was used to measure muscle strength, in isokinetic bilateral mode and with concentric contraction for the extension/flexion knee pattern, at 18 months postoperative. In addition, kinematic data were obtained and evaluated. On statistical analysis, no significant difference in muscle strength of the quadriceps muscle was found between the left involved and right uninvolved lower extremities (P = 0.124). Also, when comparing hamstring strength, no statistically significant difference was found between the left involved and right uninvolved sides (P = 0.210). Moreover, spatiotemporal gait parameters did not differ significantly between the involved and uninvolved legs (all P > 0.05), and no differences in kinematic or kinetic parameters were observed. This study reports the effects of MFC free flap harvest on the knee muscle strength and locomotion of patients. For most biomechanical parameters investigated, there was no effect (positive or negative).

Objective and Subjective Outcomes Following Radiofrequency of Inferior Turbinates in Patients with Sleep-Disordered Breathing.

Nasal obstruction is a frequent problem amongst patients with sleep-disordered breathing (SDB). Radiofrequency of the inferior turbinates (RFIT) is commonly utilized for inferior turbinate (IT) reduction but its effectiveness in SDB patients remains unproven. We aim to evaluate long-term objective and subjective nasal, olfactory and sleep outcomes following RFIT in SDB patients. Patients were assessed at baseline (T0) and at 3 months (T1), 6 months (T2) and 12 months (T3) following RFIT. At T0, T1, T2 and T3, the patients underwent objective assessments of their nasal airways and smell function and an evaluation of their quality-of-life, sinonasal, olfactory and sleep symptoms. Sleep studies were carried out at T0 and T2. Seventeen patients (with a median age of 42 years) underwent RFIT. A statistically significant objective and subjective improvement of the patients' nasal airways was demonstrated at T1. No other statistically significant changes were observed in the patients' nasal airways, smell, sleep study parameters or patient-reported outcomes at the other follow-ups. A multivariate analysis confirmed a statistically significant influence of age (older), sex (male), a higher BMI, the presence of septal deviation and the presence of allergic rhinitis in some of the studies' parameters. A statistically significant objective and subjective improvement of the patients' nasal airways was confirmed in the fitted model when considering the influence of the available variables. Our study confirms that the benefits of RFIT alone in SDB patients are limited and possibly only in the short-term period. Patient-related variables can potentially influence the final outcomes.

AI Prediction for Post-Lower Blepharoplasty Age Reduction.

Aesthetic standards vary and are subjective for each person, and artificial intelligence is booming in this era. the authors aim to provide a relatively objective assessment of the aesthetic outcomes, enhancing the decision-making process and understanding of the surgical results. 150 patients who had undergone lower blepharoplasty-related surgeries were included in our study. FaceAge software was created by our research team, which included four publicly age estimation convolution neural network (CNN) models, Amazon AWS Rekognition (Seattle, WA), Microsoft Azure Face (Redmond, WA), Face++ Detect (Beijing, China), and Inferdo face detection (New York, NY). We then first used this application for age accuracy between the real age and the age estimated by the four CNNs. Second, we used this application to estimate all the preoperative and postoperative images' age of 150 patients and evaluate the effort of lower blepharoplasty. In terms of accuracy in age prediction, all CNN models exhibited a certain degree of accuracy. For all 150 patients undergoing lower blepharoplasty-related surgeries, the surgeries' effect showed about 2 years of rejuvenation with a statistically significant difference; for the sex difference, men had more age reduction than women also with a statistically significant difference; quadrilateral blepharoplasty showed the most significant effect on anti-aging effect. By using the deep learning models, lower blepharoplasty-related surgeries actually had an effect on age reduction. The potential of deep learning models will provide quantitative evidence for the rejuvenation effects of blepharoplasty or other cosmetic surgeries.

Efficacy of Computerized vs. Traditional Cognitive Interventions for the Treatment of Chronic mTBI Symptoms Among Service Members.

Military service members (SMs) with mild traumatic brain injury (mTBI or concussion) frequently report cognitive and behavioral difficulties. Currently, military clinical guidelines recommend clinician-run, manualized cognitive rehabilitation (CR) to treat these symptoms; however, it is unclear whether this approach adequately addresses the unique needs of warfighters. Computerized cognitive training (CCT) programs represent an innovative, promising approach to treating cognitive difficulties; however, whether these programs can effectively remediate cognitive impairment in individuals with mTBI remains unclear. A total of 65 SMs with a history of at least 1 diagnosed mTBI were recruited from a military hospital. Participants received 1 of 2 interventions: Clinician-run, manualized CR (Study of Cognitive Rehabilitation Effectiveness [SCORE]; n = 37), consisting of 60 total intervention hours over 6 weeks, or CCT (n = 28), in which participants trained with either a commercial CCT (n = 14) or noncommercial CCT (n = 14), for a total of 12 hours over 4 weeks. Participants were assessed pre- and postintervention, using a combination of self-report and objective outcome measures: Key Behaviors Change Inventory (KBCI), a self-report measure of functional difficulties; Paced Auditory Serial Addition Test (PASAT), an objective cognitive assessment that measures both information processing speed and sustained and divided attention; and Symbol Digit Modalities Test (SDMT), an objective cognitive assessment that measures information processing speed. Mixed ANOVA revealed no interaction effect between intervention type and time (pre- and postassessment) on the PASAT (P = .643, ηp2 = 0.003), SDMT (P = .423, ηp2 = 0.010), or KBCI (P = .434, ηp2 = 0.010); however, there was a significant within-group main effect (time) on all 3 outcome measures (PASAT P < .001, ηp2 = 0.54; SDMT P < .001, ηp2 = 0.25; and KBCI P = .001, ηp2 = 0.15). On average, participants showed improvement over baseline on the PASAT (SCORE delta = 6.98, SD = 7.25, P < .001; CCT delta = 7.79, SD = 6.45, P < .001), SDMT (SCORE delta = 4.62, SD = 8.82, P = .003; CCT delta = 6.58, SD = 10.81, P = .003), and KBCI (SCORE delta = -3.22, SD = 7.09, P = .009; CCT delta = -2.00, SD = 4.72, P = .033). Additional analysis comparing the relative effectiveness of the 2 different CCTs revealed that while training with either program resulted in improved performance on the PASAT (P < .001, ηp2 = 0.627), SDMT (P = .003, ηp2 = 0.286), and KBCI (P = .036, ηp2 = 0.158), there was no interaction effect of CCT program type and change over time for any measure (PASAT P = .102, ηp2 = 1.00; SDMT P = .317, ηp2 = 0.038; and KBCI P = .719, ηp2 = 0.005). We showed that CCT programs do not differ in efficacy compared to clinician-run, manualized CR for treating symptoms associated with mTBI; however, exploratory analyses suggest that each approach may have distinct advantages for treating specific symptoms. Additionally, we showed that the improvement in the CCT intervention did not differ between those who trained using the commercial program vs. those who trained with the noncommercial program.

Wysiłki polityczne Somalii o przyjęcie do Wspólnoty Wschodnioafrykańskiej (EAC) jako znaczący krok na rzecz procesu stabilizacji państwa i regionu

SOMALIA’S POLITICAL EFFORTS TO JOIN THE EAST AFRICAN COMMUNITY (EAC) AS A SIGNIFICANT STEP IN THE PROCESS OF STABILIZING THE STATE AND THE REGIONOn 31 May 2023, the Extraordinary Summit of Heads of State of the East African Community (EAC) adopted the report on the verification of the accession application of the Federal Republic of Somalia, while deciding to start accession negotiations with immediate effect. The purpose of this article is to attempt to assess this decision, in the context of the admission of one of the world’s most unstable states to a sub-regional organisation that is considered the most advanced in achieving integration goals across the African continent. Due to the fact that Somalia became a member of the Community on 15 December 2023 – almost simultaneously with the submission of this text – an objective assessment of the impact of this decision is not yet possible, but it is possible to attempt to answer questions concerning the political and economic objectives of the accession process undertaken, as well as the potential consequences of its positive implementation.

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.

The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator. Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model. We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation. The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning.

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

Background and ObjectiveWe develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners. MethodsWe designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1–5 likert scoring system. ResultsObjective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data. ConclusionThis work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Swedish elite athletes’ experiences of psychotherapy for mental health concerns provided by licensed psychologists and psychotherapists: a qualitative study

This study explores elite athletes’ experiences of psychotherapy administered by Swedish licensed psychologists or psychotherapists with and without specialisation in elite sports, addressing the research question: What do elite athletes...

Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features