Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performanc

Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

Background: Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results: A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion: The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.</p

Prescription trends of disease-modifying treatments for multiple sclerosis in Iran over the past 30 years

Background: Iran, as a middle income country, is one of the places with high and rising prevalence of multiple sclerosis (MS). Regarding the substantial economic burden, reviewing the trend in prescribed disease modifying treatments (DMTs) could be of help. Here we studied the DMT information of nearly 14000 MS cases and its trends change for 30 years to improve health services to patients.Methods: The population base of this descriptive-analytical (cross-sectional) study consisted of all MS patients in the nationwide MS registry of Iran (NMSRI), up to August 1, 2021. Registrars from 15 provinces, 24 cities, 13 hospitals,8 MS associations, 16 private offices, and 7 clinics had entered the data.Results: Overall, 14316 cases were enrolled. The majority (76.1%) were female. The youngest and eldest patients were 5 and 78 years old, respectively. Diagnosis delay was under one year in most cases (median: 0, IQR: 0 - 1). Most (61.4%) had RRMS. Generally, platform injectables (IFN beta, glatiramer acetate) were the most used DMTs until 2010. It seems that introduction of newer agents (antiCD20s and oral DMTs) resulted in a decrease in the use of former drugs since around 2015. Some unusual practices are prominent such as using not approved DMTs for PPMS over the years, or administering high efficacy drugs like natalizumab for CIS. The results indicate the remaining popularity of first line injectable DMTs in female and pediatric patients.Discussion: Mean age (SD) at onset in our study (29 +/- 8.8) is near the statistics in Asia and Oceania (28 +/- 0.7). Concerns about COVID-19 had a noticeable impact on administering high efficacy drugs like rituximab an

Late-onset multiple sclerosis in Iran: A report on demographic and disease characteristics

Background: Today, it is estimated that around 5% of multiple sclerosis (MS) patients are in the late-onset category (age at disease onset ≥ 50). Diagnosis and treatment in this group could be challenging. Here, we report the latest update on the characteristics of Iranian patients with late-onset MS (LOMS). Methods: This cross-sectional study used the information provided by the nationwide MS registry of Iran (NMSRI). The registrars from 14 provinces entered data of patients with a confirmed diagnosis of MS by neurologists. Patients with disease onset at or later than 50 years of age were considered LOMS. Results: Of 20,036 records, the late-onset category included 321 patients (1.6%). The age-standardized LOMS prevalence was around 75 per 100,000 people. 215 patients (67%) were female. Median Expanded Disability Status Scale (EDSS) was 3 (interquartile range: 1.5–5). The majority of the cases (56%) suffered from relapsing-remitting (RR) course while 20% were diagnosed with primary progressive (PP) MS. Significantly higher proportion of male sex, PPMS, and higher EDSS were seen in the late-onset group compared with early-onset and adult-onset cases (p-value < 0.05). Seventy-five (23%) patients did not receive any disease-modifying treatment. Discussion: The more prominent degenerative pathology of LOMS may be the underlying mechanism of the observed differences in comparison to non-LOMS. Conclusion: There are substantial differences and knowledge gaps regarding LOMS which could be the subject of further research

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018