Surgical quality assessment of critical view of safety in 283 laparoscopic cholecystectomy videos by surgical residents and surgeons.

INTRODUCTION: Surgical quality assessment has improved the efficacy and efficiency of surgical training and has the potential to optimize the surgical learning curve. In laparoscopic cholecystectomy (LC), the critical view of safety (CVS) can be assessed with a 6-point competency assessment tool (CAT), a task commonly performed by experienced surgeons. The aim of this study is to determine the capability of surgical residents to perform this assessment. METHODS: Both surgeons and surgical residents assessed unedited LC videos using a 6-point CVS, a CAT, using an online video assessment platform. The CAT consists of the following three criteria: 1. clearance of hepatocystic triangle, 2. cystic plate, and 3. two structures connect to the gallbladder, with a maximum of 2 points available for each criterion. A higher score indicates superior surgical performance. The intraclass correlation coefficient (ICC) was employed to assess the inter-rater reliability between surgeons and surgical residents. RESULTS: In total, 283 LC videos were assessed by 19 surgeons and 31 surgical residents. The overall ICC for all criteria was 0.628. Specifically, the ICC scores were 0.504 for criterion 1, 0.639 for criterion 2, and 0.719 for the criterion involving the two structures connected to the gallbladder. Consequently, only the criterion regarding clearance of the hepatocystic triangle exhibited fair agreement, whereas the other two criteria, as well as the overall scores, demonstrated good agreement. In 71% of cases, both surgeons and surgical residents scored a total score either ranging from 0 to 4 or from 5 to 6. CONCLUSION: Compared to the gold standard, i.e., the surgeons' assessments, surgical residents are equally skilled at assessing critical view of safety (CVS) in laparoscopic cholecystectomy (LC) videos. By incorporating video-based assessments of surgical procedures into their training, residents could potentially enhance their learning pace, which may result in better clinical outcomes.

Video-based tools for surgical quality assessment of technical skills in laparoscopic procedures: a systematic review.

BACKGROUND: Quality of surgery has substantial impact on both short- and long-term clinical outcomes. This stresses the need for objective surgical quality assessment (SQA) for education, clinical practice and research purposes. The aim of this systematic review was to provide a comprehensive overview of all video-based objective SQA tools in laparoscopic procedures and their validity to objectively assess surgical performance. METHODS: PubMed, Embase.com and Web of Science were systematically searched by two reviewers to identify all studies focusing on video-based SQA tools of technical skills in laparoscopic surgery performed in a clinical setting. Evidence on validity was evaluated using a modified validation scoring system. RESULTS: Fifty-five studies with a total of 41 video-based SQA tools were identified. These tools were used in 9 different fields of laparoscopic surgery and were divided into 4 categories: the global assessment scale (GAS), the error-based assessment scale (EBAS), the procedure-specific assessment tool (PSAT) and artificial intelligence (AI). The number of studies focusing on these four categories were 21, 6, 31 and 3, respectively. Twelve studies validated the SQA tool with clinical outcomes. In 11 of those studies, a positive association between surgical quality and clinical outcomes was found. CONCLUSION: This systematic review included a total of 41 unique video-based SQA tools to assess surgical technical skills in various domains of laparoscopic surgery. This study suggests that validated SQA tools enable objective assessment of surgical performance with relevance for clinical outcomes, which can be used for training, research and quality improvement programs.

Automated semen analysis by SQA Vision® versus the manual approach-A prospective double-blind study.

Next to clinical investigations, the evaluation of male fertility relies mainly on detailed sperm analyses, for example, cell counting, motility, cell morphology and vitality testing. The manual creation of a spermiogram is time- and material-consuming. Therefore, reliable high-throughput systems that may be substituted for manual methods are urgently needed. The present study aimed to compare conventional sperm analysis performed as per WHO 5th guidelines and semen analysis performed with the SQA Vision® machine. SQA Vision® is a commercial device for automated sperm analysis. Data obtained independently by both methods were compared by statistical analyses using Bland-Altman plots and Passing-Bablok regression analyses. The analyses revealed that the results for sperm concentration and total motility were comparable. The agreement for progressive motility was poor, and there were clear deviations in the determination of normal sperm morphology. Passing-Bablok regression analyses and the consideration of the 95% confidence intervals pointed out systematic and proportional differences between the manual semen analysis and the automated approach.

Signal quality indices evaluation for robust ECG signal quality assessment systems.

As the current healthcare system faces problems of budget, staffing, and equipment, telemedicine through wearable devices gives a means of solving them. However, their adoption by physicians is hampered by the quality of electrocardiogram (ECG) signals recorded outside the hospital setting. Due to the dynamic nature of the ECG and the noise that can occur in real-world conditions, Signal Quality Assessment (SQA) systems must use robust signal quality indices (SQIs). The aim of this study is twofold: to assess the robustness of the most commonly used SQIs and to report on their complexity in terms of computational speed. A total of 39 SQIs were explored, of which 16 were statistical, 7 were non-linear, 9 were frequency-based and 7 were based on QRS detectors. With 6 databases, we manually constructed 2 datasets containing many rhythms. Each signal was labelled as 'acceptable' or 'unacceptable' (subcategories: 'motion artefacts', 'electromyogram noise', 'additive white Gaussian noise', or 'power line interference'). Our results showed that the performance of an SQI in distinguishing a good signal from a bad one depends on the type of noise. Furthermore, 23 SQIs were found to be robust. The analysis of their extraction time on 10-second signals revealed that statistics-based and frequency domain-based SQIs are the least complex with an average computational time of (mean: 1.40 ms, standard deviation: 1.30 ms), and (mean: 4.31 ms, standard deviation: 4.50 ms), respectively. Then, our results provide a basis for choosing SQIs to develop more general and faster SQAs.

Robustness of electrocardiogram signal quality indices.

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient's health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

miRNAs in lung cancer. A systematic review identifies predictive and prognostic miRNA candidates for precision medicine in lung cancer.

Lung cancer (LC) is the leading cause of cancer-related death worldwide and miRNAs play a key role in LC development. To better diagnose LC and to predict drug treatment responses we evaluated 228 articles encompassing 16,697 patients and 12,582 healthy controls. Based on the criteria of ≥3 independent studies and a sensitivity and specificity of >0.8 we found blood-borne miR-20a, miR-10b, miR-150, and miR-223 to be excellent diagnostic biomarkers for non-small cell LC whereas miR-205 is specific for squamous cell carcinoma. The systematic review also revealed 38 commonly regulated miRNAs in tumor tissue and the circulation, thus enabling the prediction of histological subtypes of LC. Moreover, theranostic biomarker candidates with proven responsiveness to checkpoint inhibitor treatments were identified, notably miR-34a, miR-93, miR-106b, miR-181a, miR-193a-3p, and miR-375. Conversely, miR-103a-3p, miR-152, miR-152-3p, miR-15b, miR-16, miR-194, miR-34b, and miR-506 influence programmed cell death-ligand 1 and programmed cell death-1 receptor expression, therefore providing a rationale for the development of molecularly targeted therapies. Furthermore, miR-21, miR-25, miR-27b, miR-19b, miR-125b, miR-146a, and miR-210 predicted response to platinum-based treatments. We also highlight controversial reports on specific miRNAs. In conclusion, we report diagnostic miRNA biomarkers for in-depth clinical evaluation. Furthermore, in an effort to avoid unnecessary toxicity we propose predictive biomarkers. The biomarker candidates support personalized treatment decisions of LC patients and await their confirmation in randomized clinical trials.

A reliable Q angle measurement using a standardized protocol.

BACKGROUND: Studies have shown that Q angle measurements were unreliable. Imaging studies have largely replaced the Q angle for measuring tibial tubercle lateralization. Creating a standardized protocol to measure the Q angle, with normative values, would provide a reliable reference without expensive imaging techniques. METHODS: Thirty men and 27 women without history of knee problems or family history of dislocating kneecaps were subjects. Exclusion criteria were: patellofemoral abnormalities upon examination. We measured the Q angles of both knees using a standardized protocol and a long-armed goniometer. These data were analyzed to calculate normative values. RESULTS: For all subjects, the mean was 14.8° (≈15°), 95% confidence interval (CI): ±5.4°. The male mean was 13.5°, 95% CI: ±5.2°. The female mean was 15.9°, 95% CI: ±4.8°. There was no significant difference between the right and left knees of the males (p = 0.52), nor of the females (p = 0.62), Beta = 0.14. The 2.4° difference between male and female means was due to the average height difference between the men and women. CONCLUSIONS: This study provides a standardized Q angle measurement protocol to assess tibial tubercle lateralization at a patient's first encounter (and intra-operatively) without resorting to expensive imaging studies. These values provide a reliable reference for clinical comparison, and will allow all clinicians and sports medicine personnel to assess tubercle lateralization with reliability and validity. When using this protocol, the term "Standard Q Angle" (SQA) should be used, to avoid confusion with other measurement protocols.

Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children.

OBJECTIVE: In the search for a reliable, cooperation-independent, noninvasive alternative to invasive intracranial pressure (ICP) monitoring in children, various approaches have been proposed, but at the present time none are capable of providing fully automated, real-time, calibration-free, continuous and accurate ICP estimates. The authors investigated the feasibility and validity of simultaneously monitored arterial blood pressure (ABP) and middle cerebral artery (MCA) cerebral blood flow velocity (CBFV) waveforms to derive noninvasive ICP (nICP) estimates. METHODS: Invasive ICP and ABP recordings were collected from 12 pediatric and young adult patients (aged 2-25 years) undergoing such monitoring as part of routine clinical care. Additionally, simultaneous transcranial Doppler (TCD) ultrasonography-based MCA CBFV waveform measurements were performed at the bedside in dedicated data collection sessions. The ABP and MCA CBFV waveforms were analyzed in the context of a mathematical model, linking them to the cerebral vasculature's biophysical properties and ICP. The authors developed and automated a waveform preprocessing, signal-quality evaluation, and waveform-synchronization "pipeline" in order to test and objectively validate the algorithm's performance. To generate one nICP estimate, 60 beats of ABP and MCA CBFV waveform data were analyzed. Moving the 60-beat data window forward by one beat at a time (overlapping data windows) resulted in 39,480 ICP-to-nICP comparisons across a total of 44 data-collection sessions (studies). Moving the 60-beat data window forward by 60 beats at a time (nonoverlapping data windows) resulted in 722 paired ICP-to-nICP comparisons. RESULTS: Greater than 80% of all nICP estimates fell within ± 7 mm Hg of the reference measurement. Overall performance in the nonoverlapping data window approach gave a mean error (bias) of 1.0 mm Hg, standard deviation of the error (precision) of 5.1 mm Hg, and root-mean-square error of 5.2 mm Hg. The associated mean and median absolute errors were 4.2 mm Hg and 3.3 mm Hg, respectively. These results were contingent on ensuring adequate ABP and CBFV signal quality and required accurate hydrostatic pressure correction of the measured ABP waveform in relation to the elevation of the external auditory meatus. Notably, the procedure had no failed attempts at data collection, and all patients had adequate TCD data from at least one hemisphere. Last, an analysis of using study-by-study averaged nICP estimates to detect a measured ICP > 15 mm Hg resulted in an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 71% and specificity of 86% for a detection threshold of nICP = 15 mm Hg. CONCLUSIONS: This nICP estimation algorithm, based on ABP and bedside TCD CBFV waveform measurements, performs in a manner comparable to invasive ICP monitoring. These findings open the possibility for rational, point-of-care treatment decisions in pediatric patients with suspected raised ICP undergoing intensive care.

Sensitive fluorescent detection of H2O2 and glucose in human serum based on inner filter effect of squaric acid-iron(III) on the fluorescence of upconversion nanoparticle.

Diabetes mellitus is an epidemic disease that it has became a worldwide public health problem. Thus, blood glucose monitoring has attracted extensive attention. Here, we report a nanosensor based on inner filter effect (IFE) between upconversion nanoparticles (UCNPs) and squaric acid (SQA)-iron(III) for the highly sensitive and selective detection of glucose levels in human serum. In this assay, GOx-catalyzed oxidization of glucose produces gluconic acid and hydrogen peroxide (H2O2). The latter can catalytically oxidize iron(II) to iron(III) which can rapidly (<1min) coordinate with the SQA to produce (SQA)-iron(III). The absorption band of (SQA)-iron(III) largely covered the emission band of UCNPs, resulting the fluorescence emission of UCNPs was effectively quenched. Therefore, the glucose can be monitored based on the formation of SQA-iron(III). Under the optimal condition, the fluorescence quenching efficiency shows a good linear response to glucose concentration in the ranges of 7-340µmol/L with a detection limit of 2.3µmol/L. The developed method has been further applied to monitor glucose levels in human serum with satisfactory results. Compared with other fluorescence methods, current method displayed high sensitivity and signal-to-noise ratio. Meanwhile, this nanosystem is also generalizable and can be easily expanded to the detection of various H2O2-involved analytes.