Assessment Of Severity Research Articles

Prevalence of Fibromyalgia and Widespread Pain in Psoriatic Arthritis: Association with Disease Severity Assessment in a Large US Registry.

The classic conception of pain etiology in rheumatologic disease is nociceptive pain - tissue injury and inflammation signaling through peripheral and central nerve fibers. But this can be mixed with other pain etiologies, including nociplastic, augmented pain experience due to central sensitization. The pain of fibromyalgia (FM) is nociplastic, occurs in 10-30% of rheumatologic disease patients, and its presence can influence disease severity assessment. 1) Ascertain the prevalence of FM and Widespread Pain (WP) in the CorEvitas psoriatic arthritis (PsA) registry as assessed by the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS) questionnaires. 2) Characterize the demographic and clinical factors associated with FM and WP. 3) Ascertain the association of FM and WP on the Clinical Disease Activity in Psoriatic Arthritis (cDAPSA) score and other disease activity measures. PsA registry patients completing the WPI/SSS questionnaires since May 2020, at their most recent visit recorded in the registry, were analyzed. The analysis included 1823 PsA patients; 11.1% fulfilled FM definition and 20.6% fulfilled WP definition. Several factors were associated with FM definition including female sex, depression/anxiety, impaired function, increased body mass index (BMI), and increased number of comorbidities. cDAPSA, patient pain and global, and tender joint count were twice as severe in patients with FM compared to those without. Fibromyalgia prevalence is elevated in PsA and is associated with elevated disease measures, confounding reliable disease assessment for treat-to-target goals. Identification of fibromyalgia as an influential contextual factor in disease assessment is recommended.

Role of transforming growth factor beta in assessment of severity of hypersensitivity pneumonitis: a single center study

BackgroundHypersensitivity pneumonitis (HP) is a more frequently diagnosed picture of diffuse parenchymal lung disease. It is an inflammation of the lung tissue, provoked by immune mechanisms, which happens to prone individuals as a reaction to a wide range of antigens. There are different degrees of fibrosis and inflammation. A group of extracellular mediators both proinflammatory and profibrotic claimed to be involved in the pathogenesis of HP. Among these mediators, a significant role is played by transforming growth factor-beta (TGF-β).AimCorrelation between the severity of hypersensitivity pneumonitis and the serum level of TGF beta.Patients and methodsSixty subjects were included in the study who were classified into 30 patients newly diagnosed with hypersensitivity pneumonitis and 30 healthy subjects served as controls. All the participants were subjected to complete history taking, physical examination, spirometry, 6-min walk distance test, HRCT, and serum levels of TGF-β.ResultsThe serum level of TGF beta is elevated in newly diagnosed HP cases (fibrotic and non-fibrotic) in relation to control participants showing statistical significance p value < 0.001, and the serum level of TGF beta in the fibrotic group of HP patients is more than that in non-fibrotic group with statistical significance p value 0.012.ConclusionThe serum level of transforming growth factor can be used in the assessment of the severity of hypersensitivity pneumonitis as regards the intensity of lung parenchymal changes.

Implementation of the International Classification of Diseases 11th revision behavioural indicators for disorders of intellectual development with co-occurring autism spectrum disorder.

The classification of mental, behavioural and neurodevelopmental disorders in the World Health Organization's International Classification of Diseases 11th revision (ICD-11) includes a comprehensive set of behavioural indicators (BIs) within the neurodevelopmental disorders grouping. BIs can be used to assess the severity of disorders of intellectual development in situations in which standardised measures of intellectual functioning and adaptive behaviours are not available or feasible. This international study examines the implementation characteristics of the BIs and compares them to standardised measures for assessing the severity of intellectual impairment and adaptive behaviours in disorders of intellectual development and autism spectrum disorder (ASD). The clinical utility of the ICD-11 and the fidelity of its application in international clinical settings were also assessed. A total of 116 children and adolescents (5-18years old) with a suspected or established diagnosis of disorders of intellectual development were included across four sites [Italy (n=18), Sri Lanka (n=19) and two sites in India (n=79)]. A principal component analysis was conducted to evaluate the application of the ICD-11 guidance for combining severity levels. Assessment using the BIs showed a higher proportion of individuals classified with mild severity, whereas the standardised measures indicated a higher proportion of severe ratings. Additionally, individuals with co-occurring ASD tended to have more severe impairments compared with those without ASD, as indicated by both BIs and standardised measures. Overall, the BIs were considered clinically useful, although more time and consideration were required when applying the guidelines for individuals with a co-occurring disorder of intellectual development and ASD. The principal component analysis revealed one principal component representing overall disorders of intellectual development severity levels. The ICD-11 BIs can be implemented as intended in international clinical settings for a broad range of presentations of individuals with neurodevelopmental disorders. Use of the BIs results in similar severity diagnoses to those made using standardised measures. The BIs are expected to improve the reliability of severity assessments in settings where appropriate standardised measures for intellectual and adaptive behaviours are not available or feasible.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Causality, Severity, Preventability and Predictability Assessments Scales for Adverse Drug Reactions: A Review

Causality, Severity, Preventability and Predictability Assessments Scales for Adverse Drug Reactions: A Review

Applications of Artificial Intelligence in Acute Abdominal Imaging.

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

Targeted Combined Endpoint Improvement in Patient and Disease Domains in Atopic Dermatitis: A Treat-to-Target Analysis of Adults with Moderate-to-Severe Atopic Dermatitis Treated with Upadacitinib.

A treat-to-target approach was recently developed to guide systemic treatment for adults with atopic dermatitis (AD). Recommendations outlined criteria for a 3-month initial acceptable treatment target and a 6-month optimal target, evaluated using global assessment of patient-reported disease severity, as well as Eczema Area and Severity Index, itch assessed on an 11-point numerical rating scale, Dermatology Life Quality Index, or Patient-Oriented Eczema Measure. Achievement of these targets with once-daily upadacitinib (15 mg and 30 mg) monotherapy was evaluated using integrated adult data from the Measure Up 1 and 2 phase 3 studies. Among the 852 patients treated with upadacitinib 15 mg or 30 mg, the 3-month initial acceptable target was achieved by >80%, >78%, and ≥87% of patients, and the 6-month optimal target was achieved by ≥53%, >61%, and >73% of patients at weeks 2, 16, and 52, respectively. Achievement of all 6 individual criteria for each of the target goals also increased over time. These findings suggest that upadacitinib 15 mg and 30 mg may help improve standards of care in patients with moderate-to-severe AD by achieving 6-month target goals at 16weeks and as early as 2 weeks for most patients.

Identification of the immune repertoire of γδ T-cell receptors in psoriasis.

The γδ Tcells are a subpopulation of Tcells that are abundantly found in the skin and mucous membranes. Their reactivity to self-antigens and ability to secrete various cytokines make them a key component in psoriasis development. Although the correlation between the immune repertoire (IR) of γδ T-cell receptors and the occurrence and severity of psoriasis remains incompletely explored, high-throughput sequencing of γδ Tcells has led to a deeper understanding of IR in psoriasis. This study investigated the differences between γδ Tcells in patients with psoriasis and healthy controls. The γδ Tcells were identified via immunofluorescence staining and a correlation analysis was performed according to the psoriasis area and severity index (PASI) scores. The IR sequencing method was used to detect IR in the γδ T-cell receptors. The findings demonstrated more skin γδ Tcells in patients with psoriasis, which were positively correlated with the PASI score. There were subtle differences in most variable (V), diversity (D) and joining (J) gene segments and VJ/VDJ combination segments between patients with psoriasis and healthy controls. However, a higher diversity of complementarity-determining region 3 (CDR3) was observed in patients with psoriasis. In summary, the IR of skin γδ Tcells was significantly altered in patients with psoriasis, and the diversity in the cell's CDR3 population is a promising biomarker for assessment of psoriasis severity.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Prospective Deep Learning-based Quantitative Assessment of Coronary Plaque by CT Angiography Compared with Intravascular Ultrasound.

Coronary computed tomography angiography provides noninvasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicenter international study compared an automated deep-learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS). The study included clinically stable patients with known coronary artery disease from 15 centers in the U.S. and Japan. An artificial intelligence (AI)-enabled plaque analysis service was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm. Mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements. Artificial intelligence enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.[ClinicalTrails.gov identifier: NCT05138289].

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score

BackgroundPneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency.MethodsThis retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance.ResultsThe predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001).ConclusionsThe BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Correlation Between Prostate Volume and Lower Urinary Tract Symptoms: A Hospital-Based Study

Background:&amp;nbsp;Lower urinary tract symptoms (LUTS) are manifestations of storage and voiding disorders involving the urinary bladder, urethra and the prostate gland. This study aimed to correlate prostate volume and lower urinary tract symptoms.&amp;nbsp; Materials and methods:&amp;nbsp;This study was a prospective cross-sectional study carried out among 120 males aged 40 years and above who presented with LUTS for prostate ultrasonography at the Radiology department within a period of six months. Informed consent and ethical approval were obtained from participants before commencement of the study. Severity of LUTS was evaluated using the International Prostate Symptom Score (IPSS). Each question was scored between 0 to 5 and the sum recorded as total IPSS score. The total score was graded as mild (0-7), moderate (8-19) or severe (20-35). Quality of life due to urinary symptoms was also evaluated and scored between 0 (delighted) and 6 (terrible). Prostate volume estimation was done by transabdominal ultrasonography using an ultrasound scanner fitted with a 3.5-MHz curvilinear transducer (Logic F6, General Electric, USA, 2017). Measurements were taken and the average values computed; volume calculation was obtained by default using the inbuilt computer algorithm in the ultrasound machine. The data obtained were statistically analyzed using the Statistical Package for Social Sciences (SPSS) software version 24 (International Business Machines, USA, 2016), and the results presented in tables, charts, and percentages. Pearson&amp;rsquo;s correlation coefficient was used to correlate between variables with P-values &amp;lt; 0.05 considered significant.&amp;nbsp; Results:&amp;nbsp;Results showed that the cumulative mean age of the participants was 65.1&amp;thinsp;&amp;plusmn;&amp;thinsp;9.6 years with majority of the participant&amp;rsquo;s between 60 and 69 years. The most common presenting LUTS was nocturia (98.0%), followed by frequency (92.7%). Overall, the mean IPSS score was 13.2 &amp;plusmn; 4.6 while quality of life (QoL) was 4.1 &amp;plusmn; 1.2. The mean prostate volume was 69.8 &amp;plusmn; 63.5cm3, with 79.2% of the subjects having a prostate volume &amp;ge; 30cm3. Pearson correlation between prostate volume and IPSS was significant (r = 0.304, p = 0.001). Conclusion:&amp;nbsp;The study established a positive significant correlation between prostate volume and International Prostate Symptoms Score (IPSS). This implies that sonographic PV assessment could be useful in objective assessment of LUTS severity and should be considered in management decisions and follow-up of patients with lower urinary tracts symptoms.&amp;nbsp;

Within-person fluctuations of fatigue in patients with a clinical diagnosis of systemic sclerosis and its relationship with mood, pain, sleep and physical activity

Objectives: To explore the within-person fluctuations of fatigue in systemic sclerosis and its association with negative affect, positive affect, pain, perceived exertion of physical activity and quality of sleep. Methods: We performed an ecological momentary assessment study in adult patients with a clinical diagnosis of systemic sclerosis. During 14 days, patients completed daily assessments of fatigue severity, negative affect, positive affect, pain, quality of sleep and perceived exertion of physical activity at four fixed time points. The day-to-day fluctuations in fatigue were quantified by the intra-individual variance and probability of acute change, capturing the magnitude and frequency of clinical relevant within-person day-to-day fluctuations, respectively. Using multilevel models, the within-person association between fatigue and the daily assessments were analysed. Results: Fifty-seven patients with systemic sclerosis participated. The mean (standard deviation) intra-individual variance was 1.08 (0.42) and the probability of acute change was mean (standard deviation) 0.40 (0.14), ranging from 0.08 to 0.77. For fatigue, a within-person variation of 51% was observed. Multilevel analyses showed that higher average levels and daily increases in negative affect, pain and perceived exertion of physical activity were associated with more fatigue, while the opposite was observed for positive affect and quality of sleep. Positive affect demonstrated the strongest association with fatigue fluctuations. Conclusion: This is the first quantitative study showing that fatigue in systemic sclerosis is characterized by a dynamic course and that approximately half of the day-to-day fluctuations within persons are clinically meaningful. Furthermore, our results indicate that integrating activities with positive impact on mood into fatigue treatment strategies might reduce the frequency of fatigue fluctuations.

Exploring antibiotic safety: A prospective observational study in a tertiary care public sector hospital

Background Antibiotics are the most commonly used drugs to treat bacterial infections; however, these life-saving drugs can also cause adverse drug reactions. In several pharmacovigilance studies, adverse drug reactions are a cause of prolonged hospitalization. The development of hypersensitivity reactions even after a negative skin test and patch test makes antibiotic-associated adverse reactions unpredictable. Owing to the scarcity of data on antibiotic desensitization, these drugs are important for pharmacovigilance. Most of the previous studies conducted were retrospective and lacked data regarding the duration of adverse drug reactions. Aim This study aimed to examine adverse drug reactions associated with antibiotics and to provide knowledge on the duration of these reactions. Method A prospective observational study was conducted on patients receiving antibiotics at a tertiary care hospital. The patients were followed until discharge, and causality and severity assessments were performed. Descriptive statistical analyses were performed to summarize the data. Results The study results show the median duration of adverse drug reactions was two days, with an interquartile range of two to four days. Most reactions (21.3%) were caused by cephalosporins. The majority (44.7%) of the reactions were skin and subcutaneous tissue disorders. The majority (78.7%) of the reactions were moderately severe and were not responsible for prolonged hospital stay. Conclusion The patients receiving antibiotics require close monitoring for early detection and management of adverse reactions to reduce the duration of hospital stay and the duration of adverse drug reactions.

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

Background:Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). Method:We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. Results:Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. Conclusions:HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

Predictive value of ferritin, IL-6, and erythropoietin in assessment of COVID-19 severity

Predictive value of ferritin, IL-6, and erythropoietin in assessment of COVID-19 severity

Loneliness in OCD and its determinants

Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive thoughts and repetitive behaviors, affecting approximately 1.3% of the population. Loneliness has serious consequences for future health outcomes. Although it has been extensively studied in depression, its prevalence in obsessive-compulsive disorder (OCD) has hardly been investigated. The current study sought to examine the association between loneliness and OCD, through an exploratory investigation of their demographic and clinical correlates. This cross-sectional study utilized data from the Netherlands Obsessive-Compulsive Disorder Association (NOCDA) study, designed to investigate determinants, course, and consequences of OCD in a large clinical sample. In this data base, a cohort of 363 OCD adult patients underwent assessment for loneliness severity, OCD symptomatology, comorbid conditions, and demographic variables. Findings reveal a high prevalence of loneliness among OCD patients, with nearly three-quarters (73.6%) experiencing elevated levels. Loneliness was associated with greater depression severity and specific demographic factors such as gender, age, and education level. However, the relationship between OCD severity and loneliness was explained by depression severity. Clinical and theoretical implications are discussed as well as limitations and directions for future research.

Assessment of Illness Severity in Adults Hospitalized With Acute Respiratory Tract Infection due to Influenza, Respiratory Syncytial Virus, or Human Metapneumovirus.

Influenza, respiratory syncytial virus (RSV), and human metapneumovirus (hMPV) are common respiratory viruses causing similar symptoms. Optimal tools to assess illness severity for these viruses have not been defined. Using the Hospitalized Acute Respiratory Tract Infection (HARTI) study data, we report symptom severity by clinician-rated clinical severity scores (CSS) in adults with influenza, RSV, or hMPV and correlations between CSS and patient-reported outcomes (PROs). HARTI was a global epidemiologic study in adults hospitalized with acute respiratory tract infections. Patients were assessed at enrollment within 24 h of admission with CSS and twice during hospitalization with CSS, Respiratory Infection Intensity and Impact Questionnaire™ (RiiQ™), and EQ-5D-5L. Data were summarized descriptively, stratified by pathogen and baseline and hospitalization characteristics. Domain (general, upper respiratory, and lower respiratory) and sign/symptom subscores are presented for CSS; sign/symptom subscores are presented for RiiQ™ results. Data from 635 patients with influenza, 248 with RSV, and 107 with hMPV were included. At enrollment, total CSS and general and lower respiratory signs/symptoms (LRS) scores were higher for RSV and hMPV than influenza. Between-pathogen differences were greatest for LRS scores. Dyspnea, rales/rhonchi, wheezing, and shortness of breath scores trended higher for RSV and hMPV than influenza. RiiQ™ scores for cough, fatigue, and short of breath were strongly correlated with corresponding clinician-rated symptoms. These findings support the use of PROs (e.g., the RiiQ™) correlating with clinician assessments to gauge patient well-being and aid patient management by accurately assessing respiratory illness severity due to RSV, hMPV, or influenza.

Local Assessment and Small Bowel Crohn’s Disease Severity Scoring using AI

We present a machine learning and computer vision approach for a localized, automated, and standardized scoring of Crohn's disease (CD) severity in the small bowel, overcoming the current limitations of manual measurements CT enterography (CTE) imaging and qualitative assessments, while also considering the complex anatomy and distribution of the disease. Two radiologists introduced a severity score and evaluated disease severity at 7.5mm intervals along the curved planar reconstruction of the distal and terminal ileum using 236 CTE scans. A hybrid model, combining deep-learning, 3-D CNN, and Random Forest model, was developed to classify disease severity at each mini-segment. Precision, sensitivity, weighted Cohen's score, and accuracy were evaluated on a 20% hold-out test set. The hybrid model achieved precision and sensitivity ranging from 42.4% to 84.1% for various severity categories (normal, mild, moderate, and severe) on the test set. The model's Cohen's score (κ=0.83) and accuracy (70.7%) were comparable to the inter-observer agreement between experienced radiologists (κ=0.87, accuracy = 76.3%). The model accurately predicted disease length, correlated with radiologist-reported disease length (r=0.83), and accurately identified the portion of total ileum containing moderate-to-severe disease with an accuracy of 91.51%. The proposed automated hybrid model offers a standardized, reproducible, and quantitative local assessment of small bowel CD severity and demonstrates its value in CD severity assessment.

Development and validation of a standard area diagram set to assess corn grey leaf spot severity and foliar fungicide control efficacy

AbstractGrey leaf spot (GLS), caused by Cercospora spp., has become a very important foliar disease for second‐crop corn season, mainly in South America. Therefore, this study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of GLS and to apply the SADs established in this study to evaluate the efficacy of fungicides for GLS control in field trials. The SADs with nine levels of severity (0.5; 2; 5; 10; 20; 30; 40; 50 and 60%) improved accuracy, precision and reliability. To evaluate the control of GLS in corn, two experiments spraying chemical fungicides were conducted, one in Rio Verde (GO) (experiment A) and another in Chapadão do Sul (MS) (experiment B). Both experiments were conducted in randomized block design, with 10 treatments and four replicates. For the severity assessment, the SADs elaborated in this study was used. From this severity data over time, disease progress curves, area under the disease progress curve (AUDPC), and yield were obtained. Based on the AUDPC and yield values, all treatments differed from the control. The fungicide fluxapyroxad + pyraclostrobin + mefentrifluconazole had the highest value, giving 72.8% control efficacy and the highest yield maintenance of 43.5%. The other treatments had an intermediate control efficacy and yield. The SADs proposed here is a useful tool for improving visual assessments of GLS severity on corn leaves and fungicides can be used for integrated disease management.