Accurate Diagnosis Research Articles

Meta-analysis of the value of dual-energy computed tomography in the diagnosis of anterior cruciate ligament injuries of the knee

BackgroundThis meta-analysis assessed the efficacy of dual-energy computed tomography (DECT) in the diagnosis of anterior cruciate ligament (ACL) injuries.MethodsThe literature search was performed up to December 8, 2023, and included a comprehensive examination of several databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Diagnostic metrics sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and a summary receiver operating characteristic (SROC) were determined using a bivariate model analysis. Heterogeneity within the data was explored through subgroup analyses, which considered variables including geographical region, use of magnetic resonance imaging (MRI), arthroscopy, and study design.ResultsThe analysis included ten studies encompassing 544 patients. DECT demonstrated substantial diagnostic utility for ACL injuries of the knee, with a sensitivity of 0.91 (95% confidence interval [CI]: 0.88–0.94), a specificity of 0.90 (95% CI: 0.81–0.95), a PLR of 9.20 (95% CI: 4.50–19.00), a NLR of 0.10 (95% CI: 0.06–0.14), a DOR of 97.00 (95% CI: 35.00–268.00), and an area under the curve (AUC) of 0.95 (95% CI: 0.93–0.97). The subgroup analyses consistently showed high diagnostic precision for ACL injuries across Asian population (sensitivity: 0.91, specificity: 0.91, PLR: 9.90, NLR: 0.09, DOR: 105.00, AUC: 0.96), in MRI subgroup (sensitivity: 0.85, specificity: 0.94, PLR: 9.57, NLR: 0.18, DOR: 56.00, AUC: 0.93), in arthroscopy subgroup (sensitivity: 0.92, specificity: 0.89, PLR: 8.40, NLR: 0.09, DOR: 94.00, AUC: 0.95), for prospective studies (sensitivity: 0.92, specificity: 0.88, PLR: 7.40, NLR: 0.09, DOR: 78.00, AUC: 0.95), and for retrospective studies (sensitivity: 0.91, specificity: 0.93, AUC: 0.93).ConclusionDECT exhibits a high value in diagnosing ACL injuries. The significant diagnostic value of DECT provides clinicians with a powerful tool that enhances the accuracy and efficiency of diagnosis and optimizes patient management and treatment outcomes.

PST-Diff: Achieving High-consistency Stain Transfer by Diffusion Models with Pathological and Structural Constraints.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images through the design of asymmetric attention mechanisms, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Intra-ampullary Papillary Tubular Neoplasm (IAPN): Clinicopathologic Analysis of 72 Cases Highlights the Distinctive Characteristics of a Poorly Recognized Entity.

The guidelines recently recognized the intra-ampullary papillary tubular neoplasm (IAPN) as a distinct tumor entity. However, the data on IAPN and its distinction from other ampullary tumors remain limited. A detailed clinicopathologic analysis of 72 previously unpublished IAPNs was performed. The patients were: male/female=1.8; mean age=67 years (range: 42 to 86 y); mean size=2.3cm. Gross-microscopic correlation was crucial. From the duodenal perspective, the ampulla was typically raised symmetrically, with a patulous orifice, and was otherwise covered by stretched normal duodenal mucosa. However, in 6 cases, the protrusion of the intra-ampullary tumor to the duodenal surface gave the impression of an "ampullary-duodenal tumor," with the accurate diagnosis of IAPN established only by microscopic correlation illustrating the abrupt ending of the lesion at the edge of the ampulla. Microscopically, the preinvasive component often revealed mixed phenotypes (44.4% predominantly nonintestinal). The invasion was common (94%), typically small (mean=1.2cm), primarily pancreatobiliary-type (75%), and showed aggressive features (lymphovascular invasion in 66%, perineural invasion in 41%, high budding in 30%). In 6 cases, the preinvasive component was pure intestinal, but the invasive component was pancreatobiliary. LN metastasis was identified in 42% (32% in those with ≤1cm invasion). The prognosis was significantly better than ampullary-ductal carcinomas (median: 69 vs. 41months; 3-year: 68% vs. 55%; and 5-year: 51% vs. 35%, P =0.047). In conclusion, unlike ampullary-duodenal carcinomas, IAPNs are often (44.4%) predominantly nonintestinal and commonly (94%) invasive, displaying aggressive features and LN metastasis even when minimally invasive, all of which render them less amenable to ampullectomy. However, their prognosis is still better than that of the "ampullary-ductal" carcinomas, with which IAPNs are currently grouped in CAP protocols (while IAPNs are kindreds of intraductal tumors of the pancreatobiliary tract, the latter represents the ampullary counterpart of pancreatic adenocarcinoma/cholangiocarcinoma).

Cardiomyopathies matter: a multistakeholder european initiative to improve patient outcomes through effective health policies

Abstract Background/Introduction Cardiomyopathies are common and affect up to 1:250 of the population. Despite conferring a risk for sudden cardiac death and substantial disease burdens that could be reduced through better awareness, detection and care, cardiomyopathy care is rarely incorporated in European and national health policies. New European Society of Cardiology cardiomyopathy guidelines (1) could substantially benefit care but need to be supported by effective policies. Cardiomyopathies Matter is a Europe-wide, multistakeholder initiative aiming to raise awareness about cardiomyopathies and to ensure they receive the policy attention they warrant from EU and national decision-makers. Methods Cardiomyopathies Matters participants include experts from the fields of cardiology, cardiac nursing, economics, and patient advocacy, coordinated by a Secretariat, representing 36 countries and 6 patient advocacy groups (PAGs). A Policy Roadmap and other publications were co-created based on i) reviews of published and grey literature, and ii) input from topic experts and PAGs through 2 group workshops, individual consultation meetings, and a modified nominal group technique reviewing successive drafts toward consensus approval. Results The Roadmap (2) identifies unmet needs and challenges throughout the patient care pathway and offers holistic EU and national policy recommendations to address these and improve patient outcomes (Figure 1). These include policy measures to support early and accurate diagnosis, access to expert multidisciplinary care, guidelines-informed disease investigation and management, holistic support and empowerment of patients and carers, and research and innovation. Key aspects for nurses and allied health professionals include the need for 1)improved knowledge and awareness to promote early diagnosis; 2)rapid referral systems to multidisciplinary care and access to specialist cardiomyopathy or heart failure nurses care coordination, 3)genetic counselling (index cases and cascade/family screening), and 4)support of patients and families. Building on existing examples of good practice at national expert centre networks (e.g. France, Spain, UK),Cardiomyopathies Matter published updated policy recommendations for the European Union mandate (3), which are now being adopted more widely. National initiatives are underway or in development in Belgium, Hungary, Italy, Netherlands, Poland, and Spain. Local actions include multistakeholder roundtables and events, national Roadmap publications, events, educational activities, and media outreach. Conclusions EU and national Cardiovascular Health Action Plans are needed, with specific attention to cardiomyopathies and other inherited CVDs. Cardiomyopathies Matter fosters collaboration between healthcare professionals, PAGs and policymakers to promote policies that ensure all patients and families have access to guidelines-informed care.Figure 1Cardiomyopathy Priorities

Bipolar Camouflage: A Cerebellar Cognitive Affective Syndrome Case Report.

The cerebellar cognitive affective syndrome is a neuropsychiatric syndrome composed of affective (anxiety, depression, euphoria, and emotional lability) and cognitive symptoms (executive, attentional, and visuospatial deficits) that was described in the 1990s. We present the case of a 49-year-old woman with a history of an acute neurological episode at the age of 28, after which she reported a change in personality, brief and alternating periods of depression, hypomania, and mixed episodes, and cognitive impairment that had a major impact on her personal and occupational level of functioning. She was initially diagnosed with bipolar disorder, but a clinical, neuropsychological, and imaging re-evaluation prompted a diagnostic reconsideration in favor of a cerebellar cognitive affective syndrome. This enabled therapeutical and prognostic refinement. Here, we discuss the diagnostic challenges of this syndrome and the implications that an accurate diagnosis has for patients.

“Iliacus muscle abscess as an unexpected cause of posterior hip pain in a healthy young adult female”: a case report

BackgroundIliacus muscle abscess is an uncommon but potentially life-threatening condition that can present with nonspecific symptoms, posing diagnostic challenges. This case report highlights the importance of considering iliopsoas abscess in patients presenting with fever and hip pain, especially in the absence of obvious risk factors or penetrating trauma. The novelty of this case lies in its atypical presentation mimicking a respiratory viral infection and musculoskeletal injury, impeding accurate diagnosis and appropriate management.Case PresentationA previously healthy 21-year-old female who had a mechanical fall 3 weeks prior presented with fever, right hip pain, and respiratory symptoms, initially suggestive of a respiratory infection and musculoskeletal injury. However, initial investigations revealing a markedly high C-reactive protein (CRP) concentration prompted further computed tomography (CT) imaging of her abdomen and pelvis, which uncovered an iliopsoas abscess presumably stemming from antecedent trauma. Subsequent CT guided aspiration along with culture-sensitive antibiotics led to successful treatment and resolution of her symptoms.ConclusionsThis case emphasizes the importance of considering iliopsoas abscess as a possible differential, even in young patients without typical risk factors. Markedly elevated inflammatory markers such as CRP concentrations can serve as a vital indicator, directing attention towards the possibility of septicemia or the presence of an occult abscess, facilitating prompt imaging and accurate diagnosis.

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Enhancing Medical Signal Processing and Diagnosis with AI-Generated Content Techniques.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

Longitudinal imaging for monitoring disease activity in late‐onset Rasmussen's encephalitis during multimodal rehabilitation and immune therapy

AbstractBackgroundRasmussen's encephalitis (RE) is a rare autoimmune encephalopathy typically manifesting in early childhood, causing unilateral autoimmune inflammation of the cerebral cortex, leading to progressive neurological deficits, notably focal epileptic seizures. The late‐onset variant of RE in adults progresses slower and presents atypical features. Despite extensive research, the etiology remains elusive, hindering accurate diagnosis and treatment options.Case PresentationWe present a biopsy‐confirmed late‐onset variant of RE case in a 71‐year‐old man with a disease course of 12 years. After the initiation of intravenous immunoglobulin therapy and immunosuppressive treatment, disease stabilization was achieved, as evidenced by clinical assessments and imaging. Initially, the affected hemisphere swelled hyperacutely, followed by years of atrophic encephalopathy stabilizing into a residual state, with emerging focal disease signs in the contralateral hemisphere. Multimodal rehabilitation and immune therapy attenuated brain atrophy and reduced signal enhancement.ConclusionsLate‐onset variant of RE rehabilitation remains underdeveloped, focusing on symptom management and functional recovery post‐surgery. Longitudinal imaging is crucial for monitoring immune therapy response in clinical practice.

Serum proteome signatures associated with liver steatosis in adolescents with obesity.

Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests. Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model. Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity). CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

ESC Pre-Test Probability Estimates for Obstructive Coronary Artery Disease: Can they be used in Brazil?

Abstract Aim Cardiovascular disease, primarily coronary artery disease (CAD), is the leading cause of mortality worldwide. Accurate diagnosis of CAD often requires pre-test probability (PTP) estimation, traditionally performed using scoring systems like the Diamond-Forrester (DF) and European Society of Cardiology (ESC) models. However, the applicability of such models in specific population may vary. This study compares the performance of DF and PTP scores in the Brazilian context, using coronary computed tomography angiography (CCTA) as a reference standard. Methods and results PTP for obstructive CAD was calculated using DF and ESC scores in 409 symptomatic patients without known CAD who underwent CCTA between 2019 and 2022. Predicted PTP was compared with actual CAD prevalence. DF overestimated CAD prevalence across age and symptom categories, while ESC showed better alignment with actual prevalence. Conclusion Our study confirms that the ESC PTP model is more appropriate than the DF model for determining PTP in the Brazilian population.

3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population.

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

Finger injuries in athletes

Hand injuries in sportives are becoming increasingly common, accounting for approximately 25% of all sports-related injuries. The recent growing popularity for sports such as climbing, which even became an Olympic discipline in 2022, is increasing hand and finger injuries in the active population. Some sports are more prone to typical hand and finger injuries. These injuries need to be treated quickly and correctly, to enable the athlete to receive optimal treatment followed by appropriate rehabilitation, in order to return to his or her sport in the best possible conditions. We have chosen to highlight five of these specific pathologies to help general practitioners and sports physicians to make an accurate diagnosis and manage or refer these injuries as best as possible.

Biochemical, Molecular, Postmortem and Histopathological Characterization of Bovine Theilerioses at Different Stages of Production and Reproduction

Theilerosis is the most important common vector (Tick) born and hemoprotozoan diseases (TBDS) which pose a serious threat to the livestock population in terms of mortality, reduced milk yield and lowered draft power. The disease is having impact on biochemistry and systemic functions affecting at different stages of production and reproduction of dairy cattle as well as there is lack of accurate diagnosis of this disease. A total 1900 cases were suspected for theileriosis on the basis of clinical signs and total 950 cases were found positive for theileriosis on the basis of blood smear examination. As per the stages of pregnancy and lactation, the cases were classified as cows in early pregnancy, cows in late pregnancy, cows in post parturient stage, cows in early and late lactation period. Biochemical alterations like increased AST, ALT and BUN level indicating liver and kidney dysfunctions in all the groups and significantly higher in post parturient group. The serum phosphorus, calcium and magnesium level decreased in all the affected groups and mostly in late pregnancy period and post parturient stage indicating metabolic disturbances during the disease which were aggravated during theileriosis. Through molecular diagnosis 114 cows (65.51%) were found positive for T. annulata and 41 cows (22.9%) were found positive for T. orientalis and 19 cows (10.9%) were found positive for both Theileria annulata and Theileria orientalis. Phylogenetic analysis revealed the accession number for Theileria annulata was OQ230445 which is having 100% similarity with puri isolate and 98% similarity with Khorda isolate and the accession number for T. orientalis was OQ 230446 having 85% similarity with Khorda isolate. On necropsy, the animals were weak, debilitated with atrophy of the muscles. In some cases, the liver was enlarged, markedly congested and in some cases yellowish tinged surface and cut edges with distension of gall bladder was evident. Abomasal ulcers were distinct and considered as pathognomonic. On histopathological examination, the liver revealed necrosis of hepatocytes with sinusoidal congestion and infiltration of mononuclear cells. In lymphnode, there was depletion of lymphocytes in lymphoid follicles with congestion and haemorrhage. Pulmonary edema was also evident in few cases. The combined approach involving biochemical, molecular, Post-mortem and histopathological characters were found to be fruitful in diagnosis of the bovine theileriosis at different stages of production and reproduction with optimum specificity.

Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research

Machine learning (ML) has emerged as a powerful tool in multiple sclerosis (MS) research, enabling more accurate diagnosis, prognosis prediction, and treatment optimization. However, the complexity of developing and deploying ML models poses challenges for domain experts without extensive programming knowledge. We propose a novel domain-specific language (DSL) that simplifies the process of selecting features, choosing appropriate ML models, and defining training rules for MS research. The DSL offers three approaches: AutoML for automated model and feature selection, manual selection for expert-guided customization, and a customizable mode allowing for fine-grained control. The DSL was implemented and evaluated using real-world MS data. By establishing task-specific DSLs, we have successfully identified workflows that enhance the filtering of ML models and features. This method is crucial in determining the T2-related MRI features that accurately predict both process speed time and walk speed. We assess the effectiveness of using our DSL to enhance ML models and identify feature importance within our private data, aiming to reveal the relationships between features. The proposed DSL empowers domain experts to leverage ML in MS research without extensive programming knowledge. By integrating MLOps practices, it streamlines the ML lifecycle, promoting trustworthy AI through explainability, interpretability, and collaboration. This work demonstrates the potential of DSLs in democratizing ML in MS and paves the way for future research in adaptive and evolving DSL architectures.

Optical frequency domain imaging (OFDI) represents a novel technique for the diagnosis of giant cell arteritis.

Giant cell arteritis (GCA) is an inflammatory vascular disease in which prompt and accurate diagnosis is critical. The efficacy of temporal artery biopsy (TAB) is limited by 'skip' lesions and a delay in histological analysis. This first-in-man ex-vivo study aims to assess the accuracy of optical frequency domain imaging (OFDI) in diagnosing GCA. 29 TAB samples of patients with suspected GCA were submerged in 0.9% sodium chloride and an OFDI catheter was passed through the lumen to create cross-sectional images prior to histological analysis. The specimens were then preserved in formalin for histological examination. Mean intimal thickness (MIT) on OFDI was measured, and the presence of both multinucleate giant cells (MNGCs) and fragmentation of the internal elastic lamina (FIEL) was assessed and compared with histology, used as the diagnostic gold standard. MIT in patients with/without histological evidence of GCA was 0.425 mm (±0.43) and 0.13 mm (±0.06) respectively compared with 0.215 mm (±0.09) and 0.135 mm (±0.07) on OFDI. MIT measured by OFDI was significantly higher in patients with histologically diagnosed arteritis compared to those without (p = 0.0195). For detecting FIEL and MNGCs, OFDI had a sensitivity of 75% and 28.6% and a specificity of 100% and 77.3% respectively. Applying diagnostic criteria of MIT > 0.20 mm, or the presence of MNGCs or FIEL, the sensitivity of detecting histological arteritis using OFDI was 91.4% and the specificity 94.1%. OFDI provided rapid imaging of TAB specimens achieving a diagnostic accuracy comparable to histological examination. In-vivo imaging may allow imaging of a longer arterial section.

Single-cell mass cytometry in immunological skin diseases

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.