Objective Quality Control Research Articles

A new method of intraoperative assessment of the dynamics of cortical blood flow using imaging photoplethysmography

Background. Intraoperative assessment of changes in cerebral blood flow is an important component of objective quality control of surgical treatment of cerebral artery aneurysms. Various techniques have been tried to solve this task, but they all have their drawbacks, which forces us to look for new ways of blood flow monitoring. We propose to use the technology of imaging photoplethysmography (IPPG) – a technically simple, contactless, safe and cheap optical method for assessing the perfusion of biological tissues.Aim. To demonstrate the possibility of using IPPG to assess the dynamics of cerebral blood flow parameters during aneurysm clipping surgery, as well as to identify early changes in blood supply to the cerebral cortex.Materials and methods. The study was carried out during six surgeries of clipping aneurysms of the anterior part of the Willis’s circle, both in the acute stage of rupture (n = 1) and in a planned manner (n = 5). The IPPG system, which is an LED illuminator in a single unit with a digital video camera, was located on a tripod 25 cm from the intervention zone. During each operation, two one-minute recordings of the illuminated surface of the cerebral cortex were performed: after dissection of the dura mater and before its suturing at the end of the main stage of the intervention. To improve the measurement accuracy, video frames of the studied area were recorded synchronously with the registration of an electrocardiogram. After recording, two IPPG parameters were calculated and compared: the amplitude of the pulsatile component and the pulse wave transit time. Thereafter, the obtained data were compared with the results of computed tomography. Statistical analysis was performed using pairwise comparison tests in the GraphPad Prism software package.Results. Clipping of cerebral vessel aneurysms are accompanied by significant changes in the parameters of cerebral blood flow. Analysis of the data for all patients revealed significant differences in IPPG parameters before and after surgery, namely, statistically significant increase in amplitude of the pulsatile component (n = 3) and decrease in pulse wave transit time (n = 5). The absence of significant changes in both parameters was found only in one patient who had mechanical damage in the cortex in the region of video recording.Conclusion. The IPPG system is capable to quantify changes in blood supply to the cortex during surgical treatment of cerebral artery aneurysms and to identify areas with either increased or decreased blood supply. In-depth studies are required to obtain additional markers of the postoperative state of cerebral blood flow.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning

PurposeInformative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination.MethodsSeveral deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model.ResultsResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials.ConclusionThe deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

An objective quality indicator for evaluating filtering of microscopic video in CASA system

Computer-Aided Sperm Analysis (CASA) has received great attention in the diagnosis of male reproductive health for its objectivity and accuracy. But its accuracy may be greatly affected by algorithms used in microscopic video preprocessing, especially algorithms for microscopic videos filtering. Owing to the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) indicators are not suitable for microscopic images, objective indicators for evaluating filtering of microscopic videos are scarce at present, leading to filtering algorithms are chosen arbitrarily. In addition, it is critical to establish quality control indicators for CASA from the perspective of image processing, but they are rarely mentioned for the reason that algorithms adopted in image processing aren’t public. To reduce influences of filtering on diagnostic results, it is imperative to establish an objective quality control indicator for evaluating filtering of microscopic video from the perspective of image preprocessing. The principle of formatting sperm tracking trajectory and the rules of grading trajectory continuity were introduced firstly. Then, impacts of filtering algorithms on the continuity of sperm trajectory were investigated and the relation between the continuity of sperm trajectory and the fluctuation of sperm segmentation thresholds was further explored. Finally, an objective indicator for appraising the quality of filtering for microscopic video was established based on SADTM (Sum of Absolute Difference between Threshold and the Mean).The proposed indicator can objectively evaluate the quality of microscopic video filtering and provide a guideline for algorithm selection, laying foundation for quality control for CASA.

EVALUATION OF THE EFFICACY OF VIRTUAL-SIMULATION TEACHING TECHNIQUES IN ENDODONTIC TREATMENT IN THE SYSTEM OF CONTINUING MEDICAL EDUCATION

This work represents a completed study on the development and implementation of the developed and optimized virtual-simulation program of continuing medical education (36 hours) to improve the manual skills of therapeutic treatment in dentistry in the module "Direct Restorations" using the evaluation of the survival rate of knowledge and skills obtained.
 With the traditional system of practical retraining of military dentists, using the dental clinic as a training base is difficult; the presence of a preceptor is required during the training manipulation; training depends on the clinic schedule and the availability of the pathology being studied; and there is no opportunity to repeat the manipulation or intervention. In addition, evaluation of the level of practical training of trainees is subjective, there is no unified system of its objective assessment [1-4]. Two stages are introduced for the trainees in the continuing medical education system: Stage 1 - assessment of the available practical skills on standard assignments, Stage 2 - assessment of the mastered practical skills. Improving the practical skills of practicing military dentists with the help of simulation training eliminates the risk to the life and health of the patient and trainee, allows you to conduct classes on an individual educational program without taking into account the working hours of the medical organization and the work schedule of the teacher, makes it possible to repeatedly practice the skill and bring the manipulation to automatism, provides objective quality control of its implementation, easily models rare pathologies and clinical cases [1-7].

An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

A Multi-Component Synthesis Scoring Method of Gridded Fusion Products for Precipitation Quality Control

A crucial and fundamental type of precipitation monitoring is the automatic observation of precipitation by ground stations. However, the typical quality control methods that are applied to rainfall present restrictions because of their discontinuities in time and space. The multi-component comprehensive consistency approach is a subjective quality control method that uses changes in other weather components associated with the precipitation process. The present study determined the reference weather components for precipitation by calculating the correlation coefficients between weather components and precipitation, and the proportions that passed the significance tests. These components included air pressure, visibility, relative humidity, wind speed, temperature difference, and the sum of cloud covers of 125–875 hPa. This study introduced various gridded fusion products to fill in the empty spaces of weather components, and put forward a scoring method to convert a subjective method into an objective method. The outcomes of the scoring method are assessed using the artificial quality control results. The evaluation’s findings demonstrate that the scoring method’s accuracy in every period of precipitation is greater than 0.75, meeting the need for objective quality control of real-time surface precipitation.

An Objective Quality Control of Surface Contamination Observations for ABI Water Vapor Radiance Assimilation

AbstractA quality control (QC) process which handles surface impacts is an important step toward successful assimilation of the Advanced Baseline Imager (ABI) water vapor (WV) band radiances. If the QC is too relaxed, many surface contaminated radiances get assimilated. If the QC is too stringent, useful radiances are rejected. Either way can result in reduced or even compromised observation impacts. A new machine learning‐based QC scheme for the three ABI WV bands is developed and optimized to help understand the importance and effectiveness of the scheme. Unlike previous schemes which are dependent on the background, this scheme extracts and blends the surface information from 7 ABI bands (bands 8–10, 13–16) to determine if a WV radiance is affected by the surface. Simulation studies show that the new QC scheme is effective in retaining radiances that are either unaffected by the surface or have very small surface contamination. It is highly effective in rejecting radiances with large surface contamination. Numerical experiments from a single case study of Hurricane Harvey (2017) were carried out to optimize the QC and to understand the potential impacts on forecasts. The use of the new QC scheme shows that radiances from each WV band have substantially added value. Combining them has a positive impact on hurricane track forecasts compared with existing QC schemes. Hence, it is critical that an optimized QC scheme is used for infrared WV radiance assimilation. It provides a balance between positive impacts from useful radiances and negative impacts from surface contaminated radiances.

An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength.

Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers.

FNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control.

Standardization and Quality Assessment Under the Perspective of Automated Computer-Assisted HEp-2 Immunofluorescence Assay Systems.

The recent availability of automated computer-assisted diagnosis (CAD) systems for the reading and interpretation of the anti-nuclear antibody (ANA) test performed with the indirect immunofluorescence (IIF) method on HEp-2 cells, has improved the reproducibility of the results and initiated a process of harmonization of this test. Furthermore, CAD systems provide quantitative expression of fluorescence intensity, allowing the introduction of objective quality control procedures to the monitoring of the entire process. The calibration of the reading systems and the automated image interpretation are essential prerequisites for obtaining reproducible and harmonized IIF test results and form the basis for standardization, regardless of the computer algorithms used in the different systems. The use of automated CAD systems, facilitating control procedures, represents a step forward for the quality certification of the laboratory.

Quality control of rigid endoscopes: a comparative study between ScopeControl® and surgeons' evaluation.

In central sterile services departments (CSSD), the functionality of rigid endoscopes, which are complex and fragile reusable devices, is usually controlled visually and is considered a complex and subjective task. ScopeControl® was developed to provide an automated quality control of rigid endoscopes by measuring the value of six parameters: viewing angle (VA), field of view (FV), color correctness (CC), light transmission (LT), fibers transmission (FT), and focus (FC). The aim of the present study was to assess the ability of ScopeControl® to pre-emptively identify endoscope defects before the surgeon considers them as defective. The same endoscope was evaluated by surgeons during surgery using a scoring scale as well as the CSSD staff using the ScopeControl® during reprocessing. The ScopeControl® categorized the endoscope into 3 groups: "passed," "in danger," and "failed." Correlations between the surgeon's evaluation and results of the ScopeControl® were calculated. One hundred sixty-six controls were carried out concerning 51 different endoscopes. According to the surgeon's evaluation, 78.9% and 80.7% of controls were considered as satisfactory for image and brightness quality, respectively. Results obtained using ScopeControl® found that 13.3% of controls were considered as "passed," 31.3% "in danger," and 55.4% "failed," with poor correlation with the surgeons' evaluation. LT and FT parameters represented 95.2% of the reasons for failures. The ability of the ScopeControl® to detect endoscope defects earlier than surgeons was validated by tracking the results of endoscopes used and controlled several times. The ScopeControl® achieved an objective and consistent quality control of endoscopes and showed poor correlation with the surgeon's opinion. In practice, the ScopeControl® could avoid the use of defective endoscopes in the surgery unit and thus improve the quality of the surgical procedure.

Multi-Institutional Prospective Validation of Prognostic mRNA Signatures in Early Stage Squamous Lung Cancer (Alliance).

Surgical resection is curative for some patients with early lung squamous cell carcinoma. Staging and clinical factors do not adequately predict recurrence risk. We sought to validate the discriminative performance of proposed prognostic gene expression signatures at a level of rigor sufficient to support clinical use. The two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen and data sets. The first stage validation confirmed a signature's ability to stratify patient survival. The second-stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled in institutional (cohort I) or cooperative group (cohort II) biospecimen and data collection protocols. All cases underwent a central review of clinical, pathologic, and biospecimen parameters using objective criteria to determine final inclusion (cohort I: n= 249; cohort II: n= 234). Primary selection required that a signature significantly predict a 3-year survival after surgical resection in cohort I. Signatures meeting this criterion were further tested in cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature. Male sex, advanced age, and higher stage were associated with shorter survival in cohort I and established a baseline clinical model. Of the three signatures validated in cohort I, one signature was validated in cohort II and statistically significantly enhanced the prognosis relative to the baseline model (C-index difference 0.122; p < 0.05). These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy

In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a capture, store, and forward model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area by using RCM and coregistered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 coregistered RCM-dermoscopic image pairs and illustrate how the results of the RCM-only model can be improved via a multimodal (RCM+ dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning-based automatic quantification offers a feasible objective quality control measure for RCM imaging.

Patient data used in internal quality control

Objective Quality control procedure based on the patient data in clinical chemistry was set up in laboratory information system (LIS). Methods Clinical chemistry tests results of outpatients and inpatients were collected from January 2016 to March 2017 in Zhejiang Provincial People′s Hospital. Statistical results of daily patient data, including , P2.5, P5, P10, P25, P50, P75, P90, P95 and P97.5 were calculated. Secondly, cumulative coefficients of variation (CV) of these statistical datawere calculated and compared to different criterions. Optimal analytes and related control concentrations were chosen. The minimum number of patient sample which use as control point was calculated by PASS 11.0 software. Finally, the quality control procedure was set up base on the LIS and was verified by patient data. Results In outpatients, was chosen as control point in AFU, APOA, APOB, CA, CL, HDL, K, MG, NA, NEFA, TP and URIC and the minimum number of sample needed were 23, 23, 30, 8, 10, 24, 34, 8, 8, 20, 13 and 22. P25 was chosen in ALP and TBIL. P50 was chosen in AST, GLU, GPDA and PHOS.P75 was chosen in ALB, CHE, CREA and DBIL. In inpatients, was chosen as control point in AFU, ALB, APOA, APOB, CA, CL, HDL, K, Lpa, MG, NA, NEFA, TP and URIC and the minimum number of sample needed were 73, 19, 34, 18, 10, 30, 36, 21, 87, 12, 17, 51, 26 and 52; P25 was chosen in ALP, ALT, AST, CREA, DBIL, LDH, TBIL and TG. P50 in PHOS, P75 in GPDA, and P90 in CHE. 200 samples were needed in the tests which used percentiles as control points. Most CVs of these control points were higher than the commercial quality control used every day. Finally, a quality control procedure based on patient data were set up in LIS. L-J and Z score charts were used to find out systematic bias. Conclusion Patient data used in internal quality control was an economical and practical way, which can make up for the deficiency of traditional method. Key words: Clinical chemistry tests; Clinical laboratory techniques; Quality control; Clinical laboratory information systems

Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts’ knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983–2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006–2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

Image‐guided radiotherapy quality control: Statistical process control using image similarity metrics

The purpose of this study was to demonstrate an objective quality control framework for the image review process. A total of 927 cone-beam computed tomography (CBCT) registrations were retrospectively analyzed for 33 bilateral head and neck cancer patients who received definitive radiotherapy. Two registration tracking volumes (RTVs) - cervical spine (C-spine) and mandible - were defined, within which a similarity metric was calculated and used as a registration quality tracking metric over the course of treatment. First, sensitivity to large misregistrations was analyzed for normalized cross-correlation (NCC) and mutual information (MI) in the context of statistical analysis. The distribution of metrics was obtained for displacements that varied according to a normal distribution with standard deviation of σ = 2 mm, and the detectability of displacements greater than 5 mm was investigated. Then, similarity metric control charts were created using a statistical process control (SPC) framework to objectively monitor the image registration and review process. Patient-specific control charts were created using NCC values from the first five fractions to set a patient-specific process capability limit. Population control charts were created using the average of the first five NCC values for all patients in the study. For each patient, the similarity metrics were calculated as a function of unidirectional translation, referred to as the effective displacement. Patient-specific action limits corresponding to 5 mm effective displacements were defined. Furthermore, effective displacements of the ten registrations with the lowest similarity metrics were compared with a three dimensional (3DoF) couch displacement required to align the anatomical landmarks. Normalized cross-correlation identified suboptimal registrations more effectively than MI within the framework of SPC. Deviations greater than 5 mm were detected at 2.8σ and 2.1σ from the mean for NCC and MI, respectively. Patient-specific control charts using NCC evaluated daily variation and identified statistically significant deviations. This study also showed that subjective evaluations of the images were not always consistent. Population control charts identified a patient whose tracking metrics were significantly lower than those of other patients. The patient-specific action limits identified registrations that warranted immediate evaluation by an expert. When effective displacements in the anterior-posterior direction were compared to 3DoF couch displacements, the agreement was ±1 mm for seven of 10 patients for both C-spine and mandible RTVs. Qualitative review alone of IGRT images can result in inconsistent feedback to the IGRT process. Registration tracking using NCC objectively identifies statistically significant deviations. When used in conjunction with the current image review process, this tool can assist in improving the safety and consistency of the IGRT process.

Metabolic discrimination of sea buckthorn from different Hippopha\xeb species by 1H NMR based metabolomics

Sea buckthorn (Hippophaë; Elaeagnaceae) berries are widely consumed in traditional folk medicines, nutraceuticals, and as a source of food. The growing demand of sea buckthorn berries and morphological similarity of Hippophaë species leads to confusions, which might cause misidentification of plants used in natural products. Detailed information and comparison of the complete set of metabolites of different Hippophaë species are critical for their objective identification and quality control. Herein, the variation among seven species and seven subspecies of Hippophaë was studied using proton nuclear magnetic resonance (1H NMR) metabolomics combined with multivariate data analysis, and the important metabolites were quantified by quantitative 1H NMR (qNMR) method. The results showed that different Hippophaë species can be clearly discriminated and the important interspecific discriminators, including organic acids, L-quebrachitol, and carbohydrates were identified. Statistical differences were found among most of the Hippophaë species and subspecies at the content levels of the aforementioned interspecific discriminators via qNMR and one-way analysis of variance (ANOVA) test. These findings demonstrated that 1H NMR-based metabolomics is an applicable and effective approach for simultaneous metabolic profiling, species differentiation and quality assessment.

QUALITY CRITERIA FOR ALGORITHMS OF TARGET DETECTION AND TRACKING IN SECURITY SYSTEMS WITH ACTIVE SENSORS

Objective quality control of the software components in security monitoring systems makes it possible to monitor the progress of its development and also to compare a finished product with analogs, which is necessary to test a product cost-efficiency. Analytical overview of approaches to control of quality of detection and tracking of targets in security systems with active sensors has been performed. Based on the analysis performed and currently applicable requirements the approach has been proposed to criteria-based evaluation of quality, which makes it possible to take a decision on whether or not the evaluated algorithm meets the applicable requirements. This approach assumes the use of a set of test data, and on the basis of the results of processing of such data the implementation of the targets detection and tracking algorithm shall be evaluated. Such evaluation includes calculation of the values of the problem-targeted quality measures. In the process of obtaining common values and standardization, the values of quality measures shall pass to the ratings of these measures. A decision on whether or not the evaluated implementation of the algorithm of target detection and tracking meets the applicable requirements shall be taken on the basis of the comparison of quality measures ratings with the threshold values.

Quality-controlled dose-reduction of pelvic X-ray examinations in infants with hip dysplasia

BackgroundDigital plain radiography (DR) examinations of the pelvis are frequently performed in infants with hip dysplasia. ObjectiveThe purpose was to reduce the radiation dose and to determine objective quality control criteria to ensure accurate assessment. This seems feasible because of higher quantum efficiency of DR and easy assessable anatomical structures for most orthopaedic measurements. Materials and methodsInstitutional review board approval was obtained. In this prospective randomized study, 264 patients underwent X-ray examination of the pelvis with standard and reduced dose. The evaluation of the plain-radiographs was conducted using the following criteria: acetabular and center edge angle, closing of the epiphyseal plates and maturation of the femoral head. Two radiologists evaluated these criteria using a score ranging from 1 (definitely assessable) to 4 (not assessable). If a single criterion had been evaluated with a score of 3 or more points or more than 2 criteria with 2 points, the radiograph was scored as “not assessable”. The statistical analysis was conducted as non-inferiority-trial. ResultsFive (1.9%) examined X-rays were scored as not assessable. There was no statistical inferiority between the examinations with standard (4.57μSv) or reduced dose (3.06μSv). Also, the individual evaluation of the defined criteria was dose-independent. ConclusionThe adequate evaluation of hip dysplasia in children and young adults on pelvic radiographs is possible with reduced radiation dose, by simple using an exposure class of 800 instead of 400.

Pelvic X-ray examinations in follow-up of hip arthroplasty or femoral osteosynthesis – Dose reduction and quality criteria

ObjectiveDigital plain radiographs of the pelvis are frequently performed in follow-up examinations of patients who received total hip arthroplasty (THA) or osteosynthesis (OS). Thus, the purpose was to reduce the radiation dose and to determine objective quality control criteria to ensure accurate assessment. Materials and methodsInstitutional review board approval was obtained. In this prospective randomized study, 289 patients underwent X-ray examination of the pelvis as follow up after receiving THA or OS with standard and reduced dose. The evaluation of the plain radiographs was conducted using the following criteria: bone–implant interface, implant–implant discrimination, implant–surface character and periarticular heterotopic ossification. Two radiologists evaluated these criteria using a score ranging from 1 (definitely assessable) to 4 (not assessable). If a single criterion had been evaluated with a score of 3 or more or more than 2 criteria with 2 points, the radiograph was scored as “not assessable“. The study was designed as non-inferiority-trial. ResultsSeven (2.4%) examined X-rays were scored as not assessable. There was no statistical inferiority between the examinations with standard (0.365mSv) or reduced dose (0.211mSv). Reduced dose only led to limitations in the evaluation of ceramic components with low clinical impact in most scenarios. ConclusionPlain radiography of the pelvis in patients with THA or OS can be performed with a dose reduction of about 42% without a loss of important information. The obtained quality control criteria were clinically applicable.