Factors Leading to Diagnostic and Therapeutic Delay of Rheumatoid Arthritis and Their Impact on Disease Outcome.

Objective To identify the factors which lead to delay in diagnosis and initiation of disease-modifying anti-rheumatic drugs (DMARDs) in rheumatoid arthritis (RA) patients and their impact on disease outcome and functional ability. Methodology This cross-sectional study was conducted from June 2021 to May 2022 at the Department of Rheumatology and Immunology, Sheikh Zayed Hospital, Lahore. Inclusion criteria were patients aged >18 years who were diagnosed with RA, based on American College of Rheumatology (ACR) criteria 2010. Delay was defined as any sort of delay which leads to delay in diagnosis or initiation of treatment of more than three months. The factors and impact on disease outcome were measured by using Disease Activity Score-28 (DAS-28) for disease activity and Health Assessment Questionnaire-Disability Index (HAQ-DI) for functional disability. The collected data were analyzed with Statistical Package for Social Sciences (SPSS) version 24 (IBM Corp., Armonk, NY, USA). Results One hundred and twenty patients were included in the study. Mean delay in referral to a rheumatologist was 36.75±61.07 weeks. Fifty-eight (48.3%) patients with RA were misdiagnosed before presentation to a rheumatologist. Sixty-six (55%) patients had the perception that RA is a non-treatable disease. Delay in diagnosis of RA from onset of symptoms (lag 3) and delay in start of DMARDs from onset of symptoms (lag 4) were significantly associated with increased DAS-28 and HAQ-DI scores (p-value 0.001). Conclusion The factors which led to diagnostic and therapeutic delay were delayed consultation with a rheumatologist, old age, low education status and low socioeconomic status. Rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) antibodies had no role in diagnostic and therapeutic delay. Many RA patients were misdiagnosed with gouty arthritis and undifferentiated arthritis before consulting a rheumatologist. This diagnostic and therapeutic delay compromises RA management leading to high DAS-28 and HAQ-DI in RA patients.

Correlation of Hypothyroidism With Disease Activity Score-28 in Patients of Rheumatoid Arthritis.

Introduction Rheumatoid arthritis (RA) is a chronic autoimmune disorder with variable disease course including periods of flares and remissions. High disease activity in terms of disease activity score-28 (DAS-28) results in significant morbidity. Hypothyroidism is found to be associated with higher DAS-28 scores in RA. This study is planned to determine overt and subclinical hypothyroidism and its correlation with the DAS-28 score in patients with RA. Methodology This study was conducted from June 2021 to March 2022 at the department of rheumatology and immunology at Shaikh Zayed Hospital, Lahore, Pakistan. Inclusion criteria were any male and female patients aged between 18 and 70 years. The blood samples of diagnosed patients with RA were sent for thyroid function tests (thyroxine [FT4], thyroid-stimulating hormone [TSH]), and erythrocyte sedimentation rate (ESR), and the patients were categorized as overt hypothyroidism, subclinical hypothyroidism, and non-hypothyroid. The collected data were analyzed on Statistical Package for the Social Sciences (SPSS) version 24.0 (IBM Corp., Armonk, NY). Results The mean age of patients was 38.18 ± 9.78 years. The mean duration of symptoms was 14.65 ± 1.04 months. There were 182 (91%) females and 18 (9%) males. The mean number of swollen joints was 2.26 ± 2.8, and the mean number of tender joints was 4.16 ± 5.11. Sixty patients (30%) had high disease activity, i.e., DAS-28 score > 5.1. Fifty-seven patients (28.5%) with RA had subclinical hypothyroidism, and 19 patients (9.5%) had overt hypothyroidism. Pain visual analog scale (VAS) and DAS-28 were significantly higher in hypothyroid patients. Conclusion It was concluded that patients of RA with concomitant hypothyroidism had increased disease activity with increased tender joints. Thyroid function tests should be included in the clinical evaluation of RA patients. The evaluation of thyroid functional status must be done during screening in RA patients. This will detect thyroid disorders earlier, with early treatment initiation and possibly a better prognosis.

Denoising proton therapy Monte Carlo dose distributions in multiple tumor sites: A comparative neural networks architecture study.

INTRODUCTION: Monte Carlo (MC) algorithms provide accurate modeling of dose calculation by simulating the delivery and interaction of many particles through patient geometry. Fast MC simulations using large number of particles are desirable as they can lead to reliable clinical decisions. In this work, we assume that faster simulations with fewer particles can approximate slower ones by denoising them with deep learning. MATERIALS AND METHODS: We use mean squared error (MSE) as loss function to train networks (sNet and dUNet), with 2.5D and 3D setups considering volumes of 7 and 24 slices. Our models are trained on proton therapy MC dose distributions of six different tumor sites acquired from 50 patients. We provide networks with input MC dose distributions simulated using 1 × 106 particles while keeping 1 × 109 particles as reference. RESULTS: On average over 10 new patients with different tumor sites, in 2.5D and 3D, our models recover relative residual error on target volume, ΔD95TV of 0.67 ± 0.43% and 1.32 ± 0.87% for sNet vs. 0.83 ± 0.53% and 1.66 ± 0.98% for dUNet, compared to the noisy input at 12.40 ± 4.06%. Moreover, the denoising time for a dose distribution is: < 9s and  < 1s for sNet vs. < 16s and  < 1.5s for dUNet in 2.5D and 3D, in comparison to about 100 min (MC simulation using 1 × 109 particles). CONCLUSION: We propose a fast framework that can successfully denoise MC dose distributions. Starting from MC doses with 1 × 106 particles only, the networks provide comparable results as MC doses with1 × 109 particles, reducing simulation time significantly.

Artificial intelligence and machine learning for medical imaging: A technology review.

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

Mitigating inherent noise in Monte Carlo dose distributions using dilated U-Net.

PURPOSE: Monte Carlo (MC) algorithms offer accurate modeling of dose calculation by simulating the transport and interactions of many particles through the patient geometry. However, given their random nature, the resulting dose distributions have statistical uncertainty (noise), which prevents making reliable clinical decisions. This issue is partly addressable using a huge number of simulated particles but is computationally expensive as it results in significantly greater computation times. Therefore, there is a trade-off between the computation time and the noise level in MC dose maps. In this work, we address the mitigation of noise inherent to MC dose distributions using dilated U-Net - an encoder-decoder-styled fully convolutional neural network, which allows fast and fully automated denoising of whole-volume dose maps. METHODS: We use mean squared error (MSE) as loss function to train the model, where training is done in 2D and 2.5D settings by considering a number of adjacent slices. Our model is trained on proton therapy MC dose distributions of different tumor sites (brain, head and neck, liver, lungs, and prostate) acquired from 35 patients. We provide the network with input MC dose distributions simulated using 1 × 10 6 particles while keeping 1 × 10 9 particles as reference. RESULTS: After training, our model successfully denoises new MC dose maps. On average (averaged over five patients with different tumor sites), our model recovers D 95 of 55.99 Gy from the noisy MC input of 49.51 Gy, whereas the low noise MC (reference) offers 56.03 Gy. We observed a significant reduction in average RMSE (thresholded >10% max ref) for reference vs denoised (1.25 Gy) than reference vs input (16.96 Gy) leading to an improvement in signal-to-noise ratio (ISNR) by 18.06 dB. Moreover, the inference time of our model for a dose distribution is less than 10 s vs 100 min (MC simulation using 1 × 10 9 particles). CONCLUSIONS: We propose an end-to-end fully convolutional network that can denoise Monte Carlo dose distributions. The networks provide comparable qualitative and quantitative results as the MC dose distribution simulated with 1 × 10 9 particles, offering a significant reduction in computation time.