Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network.

Introduction: Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations. Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification. Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Updates in diagnosis and treatment of pediatric antiphospholipid syndrome.

PURPOSE OF REVIEW: This review offers an overview of the most important recent articles on pediatric APS. RECENT FINDINGS: Non-thrombotic extra criteria manifestations were prevalent in pediatric APS. Pregnancy morbidity has been described as the first manifestation of APS at youth age, impairing gestational outcomes. The 2023 APS criteria were developed for adult APS patients, and there is still a lack of pediatric-specific APS criteria. Catastrophic APS was more commonly reported as the initial manifestation of pediatric APS than in adults. Regarding treatment, direct oral anticoagulants have been recently approval for pediatric patients with venous thrombosis. New approaches have been proposed for severe cases, for arterial thrombosis, and rituximab for refractory cases. Recurrences typically occurred early and were associated with older age at diagnosis. Current studies highlighted the multifaceted nature of pediatric APS. Further large prospective multicenter studies evaluating new medications capable of reducing recurrence risk and improving prognosis in this population will be required.

The classification algorithms to support the management of the patient with femur fracture.

Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient's length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

Percutaneous coronary intervention involving coronary bifurcation is associated with higher mortality and complications.

BACKGROUND: Percutaneous coronary intervention (PCI) in patients with bifurcation lesions is associated with higher complexity and adverse outcomes. The goal of this study was to evaluate the inpatient outcomes of patients with PCI of bifurcation lesions. METHODS: The National Inpatient Sample (NIS) database, years 2016-2020, was studied using ICD 10 codes. Patients undergoing PCI for bifurcation lesions were compared to those undergoing PCI for non-bifurcation lesions, excluding chronic total occlusion lesions. We evaluated post-procedural inpatient mortality and complications. RESULTS: PCI in patients with bifurcation lesions was associated with higher mortality and post-procedural complications. A weighted total of 9,795,154 patients underwent PCI; of those, 43,480 had a bifurcation lesion. The bifurcation cohort had a 3.79% mortality rate, and the rate in those with non-bifurcation lesions was 2.56% (OR, 1.50; CI: 1.34-1.68; P<0.001). Upon conducting multivariate analysis, which adjusted for age, sex, race, and significant comorbidities, PCI for bifurcation lesions remained significantly associated with a higher mortality rate compared to non-bifurcation lesion PCI (OR, 1.68; 95% CI, 1.49-1.88; P<0.001). Furthermore, PCI for bifurcation lesions was associated with higher rates of myocardial infarction (OR, 2.26; 95% CI, 1.68-3.06; P<0.001), coronary perforation (OR, 7.97; 95% CI, 6.25-10.17; P<0.001), tamponade (OR, 3.46; 95% CI, 2.49-4.82; P<0.001), and procedural bleeding (OR, 5.71; 95% CI, 4.85-6.71; P<0.001). Overall, post-procedural complications were 4 times more in patients with bifurcation lesions than in those without (OR, 4.33; 95% CI, 3.83-4.88; P<0.001). CONCLUSION: Using a large, national inpatient database, we demonstrate that both mortality rates and post-procedural complication rates were significantly higher in patients undergoing PCI for bifurcation lesions than in those undergoing PCI for non-bifurcation lesions.

Application of DMSFNN-COA technique for brand image design.

Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as "Stylish" and "Natural". By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as "Stylish" and "Natural". Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both "Natural" and "Stylish" classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for "Stylish" and 65 %-70 % for "Natural" product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.

Recognition of environmental contaminant and pathogenic bacteria by means of redox potential methodology.

The time-consuming nature of culturing methods has urged the exploration of rapid modern technologies. One promising alternative utilizes redox potential, which describes the oxidative changes within complex media, indicating oxygen and nutrient consumption, as well as the production of reduced substances in the investigated biological system. Redox potential measurement can detect microbial activity within 16 h, what is significantly faster than the minimum 24 h incubation time of the reference plate counting technique. The redox potential based method can be specific with selective media, but bacterial strains have unique kinetic pattern as well. The proposed method suggests evaluation of the curve shape for the differentiation of environmental contaminant and pathogenic microbial strains. Six bacterial species were used in validation (Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Listeria innocua, Listeria monocytogenes, and Listeria ivanovii). Descriptive parameters reached 98.2 % accuracy and Gompertz model achieved 91.6 % accuracy in classification of the selected 6 bacteria species.•Mathematical model (Gompertz function) and first order descriptive parameters are suggested to describe the specific shape of redox potential curves, while Support Vector Machine (SVM) is recommended for classification.•Due to the concentration dependent time to detection (TTD), pre-processing applies standardization according to the inflection point time.

Use of Endoscopic Ultrasound Elastography to Differentiate between Gastrointestinal Stromal Tumor and Leiomyoma Localized in the Upper Gastrointestinal Tract.

Background: Gastrointestinal stromal tumors (GISTs) have malignant potential. Distinction of GISTs from leiomyoma is important to the decision of follow-up or treatment for upper gastrointestinal tract subepithelial lesions (SELs). There are few studies on the evaluation of gastrointestinal SELs with endoscopic ultrasound (EUS) elastography. Aims: To evaluate the efficiency of strain ratio (SR) measurement and Giovannini's classification (Gc) by EUS elastography in differentiating GISTs from leiomyomas. Materials and methods: Twenty-three lesions with histopathological diagnoses of 13 GISTs and 10 leiomyomas were evaluated. The lesions' SR values were obtained from EUS reports retrospectively. Giovannini's classification was performed according to the elastography images recorded in the system. The effectiveness of SR and Gc in the distinction between GIST and leiomyomas was evaluated. Results: Twelve of the GISTs and 3 of the leiomyomas were with scores 4 and 5 according to Gc (p = 0.006). Gastrointestinal stromal tumors had a higher SR than leiomyomas (p = 0.001). For the diagnosis of GISTs, sensitivity/specificity/diagnostic accuracy were 92.3%/80%/87% for SR alone, 92.3%/70%/82.6% for Gc alone, and 84.6%/80%/82.6% for the use of both SR and Gc. Conclusions: This is the first study in which semi-quantitative (SR) and qualitative (Gc) methods were evaluated together for the distinction of GISTs and leiomyomas. The sensitivity of SR alone for diagnosing GIST is higher than that of Gc alone or the combination of both methods. Although SR alone does not diagnose GIST, it can be used as an auxiliary method in biopsy and follow-up decisions. How to cite this article: Erdem RE, Bektas M, Ellik ZM, et al. Use of Endoscopic Ultrasound Elastography to Differentiate between Gastrointestinal Stromal Tumor and Leiomyoma Localized in the Upper Gastrointestinal Tract. Euroasian J Hepato-Gastroenterol 2024;14(1):20-23.

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).

Background: The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). Methods: A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. Results: DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. Conclusions: A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Metal nanozymes modulation of reactive oxygen species as promising strategies for cancer therapy.

Nanozymes, nanostructured materials emulating natural enzyme activities, exhibit potential in catalyzing reactive oxygen species (ROS) production for cancer treatment. By facilitating oxidative reactions, elevating ROS levels, and influencing the tumor microenvironment (TME), nanozymes foster the eradication of cancer cells. Noteworthy are their superior stability, ease of preservation, and cost-effectiveness compared to natural enzymes, rendering them invaluable for medical applications. This comprehensive review intricately explores the interplay between ROS and tumor therapy, with a focused examination of metal-based nanozyme strategies mitigating tumor hypoxia. It provides nuanced insights into diverse catalytic processes, mechanisms, and surface modifications of various metal nanozymes, shedding light on their role in intra-tumoral ROS generation and applications in antioxidant therapy. The review concludes by delineating specific potential prospects and challenges associated with the burgeoning use of metal nanozymes in future tumor therapies.

Comparative outcomes of uncemented and cemented stem revision in managing periprosthetic femoral fractures: a retrospective cohort study.

INTRODUCTION: Periprosthetic femoral fractures (PFFs) following hip arthroplasty, especially Vancouver B2 and B3 fractures, present a challenge due to the association with a loose femoral stem, necessitating either open reduction and internal fixation or stem revision. This study aims to compare outcomes between uncemented and cemented stem revisions in managing Vancouver B2 and B3 fractures, considering factors such as hip-related complications, reoperations, and clinical outcome. METHODS: A retrospective cohort study was conducted at Danderyd Hospital, Sweden, from 2008 to 2022, encompassing operatively treated Vancouver B2 and B3 fractures. Patients were categorized into uncemented and cemented stem revision groups, with data collected on complications, revision surgeries, fracture healing times, and clinical outcomes. RESULTS: A total of 241 patients were identified. Significant differences were observed between the two groups in patient demographics, with the cemented group comprising older patients and more females. Follow up ranged from 1 to 15 years. Average follow up time was 3.9 years for the cemented group and 5.5 years for the uncemented group. The cemented stems demonstrated lower rates of dislocation (8.9% versus 22.5%, P = 0.004) and stem loosening (0.6% versus 9.3%, P = 0.004) than the uncemented method. Moreover, the cemented group exhibited shorter fracture healing times (11.4 weeks versus 16.7 weeks, P = 0.034). There was no difference in clinical outcome between groups. Mortality was higher in the cemented group. CONCLUSIONS: This retrospective study indicates that cemented stem revision for Vancouver B2-3 fractures is correlated with lower dislocation and stem loosening rates, necessitating fewer reoperations and shorter fracture healing times compared with the uncemented approach. The cemented group had a notably higher mortality rate, urging caution in its clinical interpretation.

Mapping the cited evidence of ductal carcinoma in situ from the 5th edition of the World Health Organisation classification of tumours of the breast.

AIMS: Ductal carcinoma in situ (DCIS) is recognised by the World Health Organisation (WHO) Classification of Tumours (WCT) as a non-invasive neoplastic epithelial proliferation confined to the mammary ducts and lobules. This report categorises the references cited in the DCIS chapter of the 5th edition of the WCT (Breast Tumours) according to prevailing evidence levels for evidence-based medicine and the Hierarchy of Evidence for Tumour Pathology (HETP), identifying potential gaps that can inform subsequent editions of the WCT for this tumour. METHODS AND RESULTS: We included all citations from the DCIS chapter of the WCT (Breast Tumours, 5th edition). Each citation was appraised according to its study design and evidence level. We developed our map of cited evidence, which is a graphical matrix of tumour type (column) and tumour descriptors (rows). Spheres were used to represent the evidence, with size and colour corresponding to their number and evidence level respectively. Thirty-six publications were retrieved. The cited literature in the DCIS chapter comprised mainly case series and were regarded as low-level. We found an unequal distribution of citations among tumour descriptors. 'Pathogenesis' and 'prognosis and prediction' contained the most references, while 'clinical features', 'aetiology' and 'diagnostic molecular pathology' had only a single citation each. 'Prognosis and prediction' had the greatest proportion of moderate- and high-levels of evidence. CONCLUSION: Our findings align with the disposition for observational studies inherent in the field of pathology. Our map is a springboard for future efforts in mapping all available evidence on DCIS, potentially augmenting the editorial process and future editions of WCTs.

Bloodstain classification methods: A critical review and a look to the future.

Classifying bloodstains is an essential part of Bloodstain Pattern Analysis. Various experts have developed methods. Each method considers the same basic bloodstain pattern types. These use either terminology based on the observable characteristics or the mechanistic cause of the bloodstain patterns as part of the classification process. This review paper considers ten classification methods from fourteen sources, which are used to classify bloodstain patterns. There are fundamental differences in how the patterns are classified, how differentiated the classification is, and whether the classification process uses clear, unambiguous criteria, and is susceptible to contextual bias. Experts have also reported issues with classifying bloodstains that have indistinguishable features. These differences expose key limitations with current classification methods: mechanistic terminology is too heavily relied on, and the classification process is susceptible to contextual bias. The development of an unambiguous classification method, based on directly observable characteristics within bloodstain patterns is recommended for future work.

Fault causes and its detection in standalone PV system using ANN and GEO technique.

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

A Deep Learning-Based Rotten Food Recognition App for Older Adults: Development and Usability Study.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

Applicability of combined high-frequency and contrast-enhanced ultrasound in finger extensor tendon injuries: three case reports.

BACKGROUND: By combining high-frequency and contrast-enhanced ultrasound (CEUS), the position of the severed end of a finger extensor tendon injury and the injury classification can be determined as part of a comprehensive preoperative evaluation in clinical practice. However, there have been no reports of high-frequency ultrasound combined with CEUS for the preoperative diagnosis of human finger extensor tendon injury. CASES PRESENTATION: One case of complete rupture of the extensor tendon was diagnosed by ultrasound, which was completely consistent with the surgery; one case of incomplete rupture was ultimately confirmed clinically; and one case of distal phalangeal bone base avulsion fracture with tendon contusion and missed diagnosis on the first radiographic examination was confirmed by follow-up radiographic examination. CONCLUSIONS: Different types of finger extensor tendon injuries exhibit distinctive contrast-enhanced ultrasonography findings. Combined high-frequency and contrast-enhanced ultrasound can accurately locate the position of the severed end of the finger extensor tendon injury before surgery while observing the contrast agent filling area to clarify injury classification, providing a reliable imaging basis for clinical practice and ultimately developing personalized diagnosis and treatment plans for patients to ensure minimal trauma and pain, as well as optimal treatment effects.

A new magnetic resonance imaging-based PUMCH classification system for congenital cervical malformations: devising a standardised diagnosis pathway.

OBJECTIVES: To develop an innovative magnetic resonance imaging (MRI)-based PUMCH (Peking Union Medical College Hospital) classification system aimed at standardising the diagnosis of congenital cervical malformations (CCMs) by identifying their distinctive MRI features. METHODS: Seventy-nine consecutive patients with CCM underwent pre-treatment pelvic MRI; three experienced gynaecological radiologists retrospectively analysed these images. Qualitative assessments included Rock et al's classification; PUMCH classification; haematometra; cervical signal features; ovarian endometriosis; haematosalpinx; and uterine, vaginal, urinary, and musculoskeletal malformations. Quantitative assessments involved the uterine volume, sagittal cervical length, and maximum ovarian cross-sectional area. The surgical treatment types were also recorded. Statistical methods were used to incorporate differences in clinical features and surgical methods into our classification. RESULTS: Morphologically, CCMs were categorised into three types: type I (53%) was characterised by the presence of a cervix with visible cervical canals; type II (23%) featured an existing cervix with concealed cervical canals; and type III (24%) indicated cervical aplasia, which involves a blind end in the lower part of the uterine corpus. Haematometra was significantly more prevalent in patients with type I CCM than in those with type II (p < 0.001). There were three cervical signal patterns: no signal (27%), no evident layer differentiation (21%), and multi-layer differentiation with haematocele (52%). Most patients (94%) had complete vaginal atresia. Type I CCM patients had a higher likelihood of regaining normal uterovaginal anatomy compared to types II and III. CONCLUSIONS: Our proposed PUMCH classification system has a high potential for enhancing the efficiency of clinical diagnosis among patients with CCM. CRITICAL RELEVANCE STATEMENT: The proposed new PUMCH classification promised to elevate the conventional diagnostic trajectory for congenital cervical malformations, offering a valuable framework to refine the selection and planning of surgical interventions, thereby enhancing overall clinical efficacy. KEY POINTS: Effective classification of congenital cervical malformations is desirable to optimise the diagnostic process. We presented a PUMCH classification of congenital cervical malformations using pelvic MRI. The new classification significantly aids clinical triage for congenital cervical malformations.

Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata.

BACKGROUND: Sexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms. METHODS: We used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010-2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model. RESULTS: Our study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25-34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p < 0.01). Out of 21 metadata, the integration of demographic and dermatological metadata led to the most significant improvement in model performance, increasing AUC by 6.7% compared to the baseline Image-only model. CONCLUSIONS: The Image + Metadata model outperformed the Image-only model in distinguishing STIs from other skin conditions. Using it as a screening tool in a clinical setting may require further development and evaluation with larger datasets.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.

BACKGROUND: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. METHODS: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. RESULTS: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. CONCLUSIONS: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.