A systematic review of instrumented assessments for upper limb function in cerebral palsy: current limitations and future directions.

INTRODUCTION: Recently, interest in quantifying upper limb function in cerebral palsy has grown. However, the lack of reference tasks and protocols, have hindered the development of quantified movement analysis in clinical practice. This study aimed to evaluate existing instrumented assessments of upper limb function in cerebral palsy, with a focus on their clinical applicability, to identify reasons for the lack of adoption and provide recommendations for improving clinical relevance and utility. METHODS: A systematic review was conducted by a multidisciplinary team of researchers and clinicians (Prospero CRD42023402382). PubMed and Web of Science databases were searched using relevant keywords and inclusion/exclusion criteria. RESULTS: A total of 657 articles were initially identified, and after the selection process, 76 records were included for analysis comprising a total of 1293 patients with cerebral palsy. The quality assessment of the reviewed studies revealed a moderate overall quality, with deficiencies in sample size justification and participant information. Optoelectronic motion capture systems were predominantly used in the studies (N = 57/76). The population mainly consisted of individuals with spastic cerebral palsy (834/1293) with unilateral impairment (N = 1092/1293). Patients with severe functional impairment (MACS IV and V) were underrepresented with 3.4% of the 754 patients for whom the information was provided. Thirty-nine tasks were used across the articles. Most articles focused on unimanual activities (N = 66/76) and reach or reach and grasp (N = 51/76). Bimanual cooperative tasks only represented 3 tasks present in 4 articles. A total of 140 different parameters were identified across articles. Task duration was the most frequently used parameter and 23% of the parameters were used in only one article. CONCLUSION: Further research is necessary before incorporating quantified motion analysis into clinical practice. Existing protocols focus on extensively studied populations and rely on costly equipment, limiting their practicality. Standardized unimanual tasks provide limited insights into everyday arm use. Balancing methodological requirements and performance evaluation flexibility is a challenge. Exploring the correlation between outcome parameters and therapeutic guidance could facilitate the integration of quantified movement assessment into treatment pathways.

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. OBJECTIVE: This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. METHODS: The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. RESULTS: The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. CONCLUSIONS: Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

Adaptability and clinical applicability of UFS-QoL in Chinese women with uterine fibroid.

OBJECTIVE: To demonstrate the applicability and adaptability of uterine fibroid symptoms and quality of life (UFS-QoL) in assessing the efficacy of treatment in Chinese populations. METHODS: This is a secondary analysis of a prospective cohort study involving 20 Chinese hospitals and 2,411 Chinese women with fibroids. Patients completed UFS-QoL and short form-36 (SF-36) at pre-surgery, 6-month and 12-month post-treatments. Internal consistency of the quality of life assessed by the UFS-QoL questionnaire using Cronbach's α coefficient (α). Principal axis factor analysis with orthogonal rotation was established to investigate relationships between items and subscales. Concurrent validity refers to the Spearman's correlation estimate of the correlation between UFS-QoL and SF-36. Using effect size and standardized response mean, the ability to detect change was evaluated by comparing pre- and post-6-month and post-12-month treatment scores. RESULTS: Exploratory factor analysis yielded six subscales (concern, activities, energy/mood, control, self-consciousness, and sexual function) with eigenvalues > 1 in UFS-QoL. A 63.61% total variance was explained by the test items. Ceiling effects of self-consciousness and sexual functioning subscales from UFS-QoL were > 15%. UFS-QoL showed a positive and moderate correlation with SF-36 to establish good concurrent validity. And showed good consistency reliability (Cronbach α > 0.7 in all subscales), ability to detect change after treatment. This excluded self-consciousness (α = 0.56), which demonstrated the lowest effect size (0.38) and standardized response means (0.38) 6- and 12-months post-treatment. CONCLUSIONS: Symptom severity, activity, and mood subscales of the Chinese UFS-QoL were valid and reliable. However, the self-consciousness domain needs further investigation on cultural adaptation, such as cognitive debriefing for how Chinese interpret these questions.

Breast Cancer-Delivered Exosomal miRNA as Liquid Biopsy Biomarkers for Metastasis Prediction: A Focus on Translational Research with Clinical Applicability.

Metastasis represents the most important cause of breast cancer-associated mortality. Even for early diagnosed stages, the risk of metastasis is significantly high and predicts a grim outcome for the patient. Nowadays, efforts are made for identifying blood-based biomarkers that could reliably distinguish patients with highly metastatic cancers in order to ensure a closer follow-up and a more personalized therapeutic method. Exosomes are nano vesicles secreted by cancer cells that can transport miRNAs, proteins, and other molecules and deliver them to recipient cells all over the body. Through this transfer, cancer cells modulate their microenvironment and facilitate the formation of the pre-metastatic niche, leading to sustained progression. Exosomal miRNAs have been extensively studied due to their promising potential as prognosis biomarkers for metastatic breast cancer. In this review, we tried to depict an overview of the existing literature regarding exosomal miRNAs that are already validated as potential biomarkers, and which could be immediately available for the clinic. Moreover, in the last section, we highlighted several miRNAs that have proven their function in preclinical studies and could be considered for clinical validation. Considering the lack of standard methods for evaluating exosomal miRNA, we also discussed the challenges and the technical aspects underlying this issue.

An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and requires trained laboratory personal, diagnosis using chest X-ray (CXR) is a befitting option. OBJECTIVE: In this study, we proposed an interpretable multi-task system for automatic lung detection and COVID-19 screening in chest X-rays to find an alternate method of testing which are reliable, fast and easily accessible, and able to generate interpretable predictions that are strongly correlated with radiological findings. METHODS: The proposed system consists of image preprocessing and an unsupervised machine learning (UML) algorithm for lung region detection, as well as a truncated CNN model based on deep transfer learning (DTL) to classify chest X-rays into three classes of COVID-19, pneumonia, and normal. The Grad-CAM technique was applied to create class-specific heatmap images in order to establish trust in the medical AI system. RESULTS: Experiments were performed with 15,884 frontal CXR images to show that the proposed system achieves an accuracy of 91.94% in a test dataset with 2,680 images including a sensitivity of 94.48% on COVID-19 cases, a specificity of 88.46% on normal cases, and a precision of 88.01% on pneumonia cases. Our system also produced state-of-the-art outcomes with a sensitivity of 97.40% on public test data and 88.23% on a previously unseen clinical data (1,000 cases) for binary classification of COVID-19-positive and COVID-19-negative films. CONCLUSION: Our automatic computerized evaluation for grading lung infections exhibited sensitivity comparable to that of radiologist interpretation in clinical applicability. Therefore, the proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Clinical Applicability of a Textile 1-Lead ECG Device for Overnight Monitoring.

Even for 1-lead electrocardiography (ECG), single-use gel conductive electrodes are employed in a clinical setting. However, gel electrodes show limited applicability for long-term monitoring due to skin irritation and detachment. In the present study, we investigated the validity of a textile ECG-belt suitable for long-term measurements in clinical use. In order to assess the signal quality and validity of the ECG-belt during sleep, 242 patients (186 males and 56 females, age 52 (interquartile range 42-60) years, body mass index 29 (interquartile range 26-33) kg·m-2) with suspected sleep apnoea underwent overnight polysomnography including standard 1-lead ECG recording. The single intervals between R-peaks (RR-intervals) were calculated from the ECG-signals. We found a mean difference for average RR-intervals of -2.9 ms, a standard error of estimate of 0.39%, as well as a Pearson r of 0.91. Furthermore, we found that the validity of the ECG-belt decreases when lying on the side, which was potentially due to the fitting of the belt. In conclusion, the validity of RR-interval measurements using the ECG-belt is high and it may be further improved for future applications by optimizing wear fitting.

[Clinical application evaluation and revision suggestions of clinical practice guideline on traditional Chinese medicine therapy alone or combined with antibiotics for community acquired pneumonia].

To clarify the clinical application of the group standard (T/CACM 1035-2017) of the Chinese Society of Traditional Chinese Medicine (TCM), the clinical practice guideline on traditional chinese medicine therapy alone or combined with community acquired pneumonia, and to understand the clinical applicability of the Guideline. The clinical workers trained in terms of the Guideline in hospitals at all levels in China were selected as the research objects. A total of 494 questionnaires on application evaluation and 511 questionnaires on applicability evaluation were collected to construct the database of the post-effect evaluation of the Guideline. Excel software was used for statistical analysis. The overall evaluation of the Guideline was 92.31%, 91.06%, 87.45% respectively in efficacy, safety and economy. The Guideline was well used in clinical application, and 99.41% of the patients were willing to follow the recommended scheme. The agreed ratio in rationality evaluation was 97.98%, 92.37%, 94.53% and 92.71% in treatment rules, syndrome differentiation and classification, prevention of complications, and rehabilitation method. The effective rate of the prescriptions recommended in the Guideline was all above 65%. More than 80% of the prescriptions were Tanreqing Injection, Yinqiao Powder, Qingjin Huatan Decoction, Maxing Shigan Decoction, Shengmai San and Shashen Maidong Decoction. Adverse reactions, unknown active components and economy of Chinese patent medicines were the important factors affecting drug use and efficacy, providing a clinical basis for updating and revising the standard.

Predictive Model of CK7 Expression in Patients With Clear Cell Renal Cell Carcinoma by Combined Multimodal Ultrasound Diagnostic Techniques: A Retrospective Study.

OBJECTIVE: The aim of the work described here was to develop and validate a predictive model for cytokeratin 7 (CK7) expression in clear cell renal cell carcinoma (ccRCC) patients by combining multimodal ultrasound diagnostic techniques. METHODS: This retrospective study enrolled 157 surgically confirmed ccRCC patients. All patients underwent pre-operative multimodal ultrasound diagnostic examinations, including B-mode ultrasound (US), color Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The patients were randomly divided into a training group (103 cases) and a testing group (54 cases). Univariate and multivariate logistic regression analyses were performed in the training group to identify independent indicators associated with CK7 positivity. These indicators were included in the predictive model. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the model's discriminative ability and accuracy. Decision curve analysis (DCA) and nomogram visualization were used to assess the clinical utility of the predictive model. RESULTS: Univariate logistic regression analysis revealed that US and CDFI observations were not correlated with CK7 expression and could not predict it. Multivariate logistic regression analysis identified age (odds ratio [OR] = 0.953, 95% confidence interval [CI]: 0.909-0.999), wash-in pattern (OR = 0.180, 95% CI: 0.063-0.513) and enhancement homogeneity (OR = 11.610, 95% CI: 1.394-96.675) as independent factors related to CK7 positivity in ccRCC. Incorporating these variables into the predictive model resulted in areas under the receiver operating characteristic curve of 0.812 (95% CI: 0.711-0.913) for the training group and 0.792 (95% CI: 0.667-0.924) for the testing group. The calibration curve and DCA revealed that the model had good accuracy and clinical utility of the model. CONCLUSION: The combination of multimodal ultrasound diagnostic techniques in constructing a predictive model for CK7 expression in ccRCC patients has significant predictive value.

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.

The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a meta-epidemiological study.

OBJECTIVES: To systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH). STUDY DESIGN AND SETTING: A meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: Twenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias. CONCLUSION: Current SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.

Early experience of pre- and post-contrast 7.0T MRI in brain tumors.

We investigated the safety and clinical applicability of 7.0 Tesla (T) brain magnetic resonance imaging (MRI) in patients with brain tumors. Twenty-four patients with intraaxial or extraaxial brain tumors were enrolled in this study. 7.0T MRIs of T2*-weighted axial and T1-weighted coronal or sagittal images were obtained and compared with 1.5T brain MRIs. The T2*-weighted images from 7.0T brain MRI revealed detailed microvasculature and the internal contents of supratentorial brain tumors better than that of 1.5T brain MRI. For brain tumors located in parasellar areas or areas adjacent to major cerebral vessels, flow-related artifacts were exaggerated in the 7.0T brain MRIs. For brain tumors adjacent to the skull base, susceptibility artifacts in the interfacing areas of the paranasal sinus and skull base hampered the aquisition of detailed images and information on brain tumors in the 7.0T brain MRIs. This study shows that 7.0T brain MRI can provide detailed information on the intratumoral components and margins in supratentorial brain tumors. Further studies are needed to develop refined MRI protocols for better images of brain tumors located in the skull base, parasellar, and adjacent major cerebrovascular structures.

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective.

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI.

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations. Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment. Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model. Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

The eEgg: Evaluation of a New Device to Measure Pain.

Aim: The aim of this study was to evaluate whether pain stimuli can be measured validly and reliably by the eEgg (electronic Egg), a new device to measure pain intensity, in comparison to the hand dynamometer. Methods: This study consists of screening and diagnostic tests conforming to the standard criterion of handgrip strength measurement. Fifty healthy participants (25 women, 25 men; age, 39.1 ± 13.7 years) participated in this study. The approach of intermodal comparison was used to transfer different degrees of pain sensations into measurable handgrip strength values. This included an intensity comparison of 10-100% of the subjective maximum handgrip strength and an application of thermal stimuli of 34-48°C. The eEgg was compared to the numeric rating scale (NRS) as a categorization method regarding the subjective assessment of pain. An online questionnaire was distributed to test the evaluation of the product's features. Results: Regarding the experiment's validity, the handgrip strength values showed significant (p < 0.05) positive correlations between the eEgg and the hand dynamometer (intensities: r=0.328 to r=0.550; thermal stimuli: r=0.353 to r=0.614). The reliability results showed good to very good correlations (p < 0.05) in the calculated ICC (intraclass correlation coefficient) values between the individual measurement devices: eEgg intensities: ICC=0.621 to 0.851; thermal stimuli: ICC=0.487 to 0.776 and hand dynamometer intensities: ICC= 0.789 to 0.974; thermal stimuli: ICC=0.716 to 0.910. Conclusion: The new eEgg device shows strong correlations with the hand dynamometer. The central limitation focuses on the obligatory use of an arbitrary unit (AU) for the eEgg. The results of the study indicate that this device can be used in medical and therapeutic practice in the future.

The Quality and Clinical Applicability of Recommendations in Ostomy Guidelines: A Systematic Review.

Aim: To systematically search ostomy clinical practice guidelines, critically assess their quality and clinical applicability of recommendations, and summarize the recommendations. Design: Systematic review. Data Sources: The PubMed, ProQuest and CINAHL databases, eight guideline databases, and three ostomy institution websites were searched on September 3, 2021. Review Methods: Appraisal of Guidelines for Research and Evaluation II (AGREE II) and AGREE Recommendation EXcellence (AGREE-REX) were used to assess the guideline. Results: The initial search identified 1475 documents. Of these, 27 full-text documents were reviewed. Finally, 10 guidelines were included. Among these, the 2019 Registered Nurses' Association of Ontario (RNAO) guidelines had the highest total scores using AGREE II and AGREE-REX. The 2019 National Institute for Health and Care Excellence (NICE) and 2018 European Hernia Society (EHS) were also ranked as high-quality and evaluated as "recommended." The median of the "applicability" domain was the lowest (45%) among the six AGREE II domains. The median of the "values and preferences" domain was the lowest (38%) among the three AGREE-REX domains. In total, 172 recommendations were summarized and parastomal hernia received the most attention among the recommendations. Conclusion: The quality of the 10 clinical practice guidelines varied widely. The three identified high-quality guidelines might be appropriate first choices in daily ostomy care and management practice and can be tailored to the local context. Ostomy guidelines require further improvement in the "applicability" and "values and preferences" domains. No Patient or Public Contribution: This review only searched and evaluated relevant documents, so such details do not apply to this review.

Modified nanofat grafting: Stromal vascular fraction simple and efficient mechanical isolation technique and perspectives in clinical recellularization applications.

Background: Nanofat grafting (NG) is a simple and cost-effective method of lipoaspirates with inter-syringe passages, to produce stromal vascular fraction (SVF) and isolate adipose-derived stem cells (ASCs). This represents a tremendous interest in the future clinical needs of tissue engineering. In this study, we optimized the NG technique to increase the yield of ASC extractions. Methods: We analyzed three groups of SVF obtained by 20, 30, and 40 inter-syringe passages. The control group was an SVF obtained by enzymatic digestion with Celase. We studied their cell composition by flow cytometry, observed their architecture by confocal microscopy, and observed immunomodulatory properties of the ASCs from each of the SVFs by measuring inflammatory markers of macrophages obtained by an ASC monocyte co-culture. Results: We have established the first cell mapping of the stromal vascular fraction of adipose tissue. The results showed that SVF obtained by 20 inter-syringe passages contains more statistically significant total cells, more cells expressing the ASC phenotype, more endothelial cells, and produces more CFU-F than the SVF obtained by 30 and 40 passages and by enzymatic digestion. Confocal microscopy showed the presence of residual adipocytes in SVF obtained by inter-syringe passages but not by enzymatic digestion. The functional study indicates an orientation toward a more anti-inflammatory profile and homogenization of their immunomodulatory properties. Conclusion: This study places mechanically dissociated SVF in the center of approaches to easily extract ASCs and a wide variety and number of other progenitor cells, immediately available in a clinical setting to provide both the amount and quality of cells required for decellularized tissues.

Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images.

Purpose: Automated algorithms for liver parenchyma segmentation can be used to create patient-specific models (PSM) that assist clinicians in surgery planning. In this work, we analyze the clinical applicability of automated deep learning methods together with level set post-processing for liver segmentation in contrast-enhanced T1-weighted magnetic resonance images. Methods: UNet variants with/without attention gate, multiple loss functions, and level set post-processing were used in the workflow. A multi-center, multi-vendor dataset from Oslo laparoscopic versus open liver resection for colorectal liver metastasis clinical trial is used in our study. The dataset of 150 volumes is divided as 81:25:25:19 corresponding to train:validation:test:clinical evaluation respectively. We evaluate the clinical use, time to edit automated segmentation, tumor regions, boundary leakage, and over-and-under segmentations of predictions. Results: The deep learning algorithm shows a mean Dice score of 0.9696 in liver segmentation, and we also examined the potential of post-processing to improve the PSMs. The time to create clinical use segmentations of level set post-processed predictions shows a median time of 16 min which is 2 min less than deep learning inferences. The intra-observer variations between manually corrected deep learning and level set post-processed segmentations show a 3% variation in the Dice score. The clinical evaluation shows that 7 out of 19 cases of both deep learning and level set post-processed segmentations contain all required anatomy and pathology, and hence these results could be used without any manual corrections. Conclusions: The level set post-processing reduces the time to create clinical standard segmentations, and over-and-under segmentations to a certain extent. The time advantage greatly supports clinicians to spend their valuable time with patients.

The Clinical Consistency and Utility of ICD-11 Diagnostic Guidelines for Gaming Disorder: A Field Study Among the Chinese Population.

Purpose: As a new category proposed in the International Classification of Diseases (11th Revision) (ICD-11), the reliability and clinical utility of ICD diagnostic guidelines for gaming disorder (GD) in the Chinese population have not been studied. The purpose of this field study is to clarify the reliability, clinical utility, and cultural applicability of ICD diagnostic guidelines for GD in China and its comparability with Internet GD (IGD) in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5). Methods: Participants included 21 paired clinical raters consisting of seven psychiatrists and 200 gaming players aged from 15 to 18 years with different risk levels of Internet addiction based on the scores of Young's Internet Addiction Test. Each participant received a semi-structured face-to-face interview by paired clinical raters at the same time. Then clinical raters made the diagnosis and filled the clinical utility questionnaire independently according to the diagnostic guidelines for GD in both ICD-11 and DSM-5. Results: The diagnostic consistency coefficient (kappa value) between the paired clinical raters was 0.545 (0.490-0.600, p < 0.001) and 0.622 (0.553-0.691, p < 0.001) for ICD-11 and DSM-5 diagnostic guidelines, respectively, for GD. The diagnostic consistency was 0.847 (0.814-0.880, p < 0.001) between GD in ICD-11 and IGD in DSM-5. Meanwhile, 86.7% of responses that agreed with the ICD-11 diagnostic guidelines for GD provided enough detailed implementation characteristics and showed good overall clinical applicability (86.0%), specificity (94.4%), usefulness (84.1%), and acceptable cultural adaptation (74.8%). GD in ICD-11 was slightly more accepted than IGD in DSM-5 (p < 0.001), while the clinical efficiency of ICD-11 was inferior to that of DSM-5 (p < 0.001). Conclusion: This study indicates that the ICD-11 diagnostic guidelines for GD have acceptable clinical reliability and high consistency with IGD in DSM-5. Their clinical applicability and cultural adaption are comparable with those of DSM-5. Although the guidelines still need to be adjusted for better implementation in China, this is already a great step committed to reducing the serious consequences caused by excessive gaming behaviors through effective identification and normative diagnosis, especially for adolescents.

An instrument for evaluating the clinical applicability of guidelines.

OBJECTIVE: To establish an instrument for evaluating the clinical applicability of guidelines from the guideline-users' perspective. METHODS: We established this instrument through forming a working group, forming an initial list of items based on a qualitative systematic review, establishing initial instrument via two rounds of modified Delphi surveys, and external review the initial instrument. RESULTS: The results of modified Delphi surveys establishing appraisal aspects, appraisal items, general information of the evaluator met the preset requirements. The instrument includes three parts: general information of the evaluator (12 items), evaluation of clinical applicability (12 items, including items on the availability, readability, acceptability, feasibility, and overall applicability of guideline), and scoring scheme. CONCLUSIONS: The instrument for evaluating the clinical applicability of guidelines from the guideline-users' perspective provides criteria and methods for improving the clinical applicability of guidelines during development and updating.

Simultaneous fluorescent detection of multiplexed miRNA of liver cancer based on DNA tetrahedron nanotags.

The level of miRNA-21, miRNA-122, and miRNA-223 are always elevated when liver cancer is present at an early stage. In this paper, a novel assay to simultaneous detect miRNA-21, miRNA-122, and miRNA-223 was proposed based on DNA tetrahedron nanotags and fluorescence resonance energy transfer (FRET), which used a single laser stimulate wavelengh from one nucleic acid stain TOTO-1 to three diverse organic dyes (Cy3, Cy3.5, Cy5). In brief, a DNA tetrahedral nanostructure (DTN) was designed with three adaptor oligos on its vertices. TOTO-1, as a fluorescent donor, can imbed into native nucleic acid backbone of DTN. Three organic dye-functionalized strands (FRET oligos) are fluorescent receptors. In the presence of target miRNAs, they can be hybridized with FRET oligos and adaptor oligos on the vertices of DTN and the stable DNA tetrahedron nanotags are formed. As a result, the confinement of TOTO-1 is in close proximity to three fluorescence dyes, the FRET between TOTO-1 and three fluorescence dyes is generate efficiently in DNA tetrahedron nanotags. Point-of-care clinical applicability is demonstrated by sensitive multiplexed quantification of three miRNAs in 10% human serum samples.