Predicting the development of rheumatoid arthritis (RA) in an early stage through magnetic resonance imaging (MRI) can initiate timely treatment and improve long-term patient outcomes. Although... Show morePredicting the development of rheumatoid arthritis (RA) in an early stage through magnetic resonance imaging (MRI) can initiate timely treatment and improve long-term patient outcomes. Although manual prediction is time-consuming and requires expert knowledge, automatic RA prediction has not been fully investigated. While standard models fail to achieve acceptable performance, we present a consistency-based deep learning framework to classify and predict RA automatically and precisely, including an output-standardized model, customized self-supervised pretraining and a loss function that is based on label consistency between original and augmented inputs. For training and evaluation, we used a database, containing 5945 MRI scans of carpal, metacarpophalangeal (MCP), and metatarsophalangeal (MTP) joints, from 2151 subjects obtained over a period of ten years. Four (three classification- and one prediction-) tasks were defined to distinguish two patient groups (with recent-onset arthritis and clinically suspect arthralgia) from healthy controls and RA from other arthritis patients within the recent-onset arthritis group, and predict RA development in a period of two years within the clinically suspect arthralgia group. The proposed method was evaluated with the area under the receiver operating curve (AUROC) on a separate test set, achieving mean AUROCs of 83.6%, 83.3%, and 69.7% in the three classification tasks, and 67.8% in the prediction task. This proves the existence of early signs of RA in MRI and the potential of a consistency-based deep learning model to detect these early signs and predict RA Show less
Hassanzadeh, T.; Shamonin, D.P.; Li, Y.L.; Krijbolder, D.I.; Reijnierse, M.; Helm-van Mil, A.H.M.V. van der; Stoel, B.C. 2024
Rheumatoid Arthritis (RA) is an autoimmune disease that mainly affects joints in the wrist and hands. It typically results in inflamed and painful joints. MRI is one of the most common imaging...Show moreRheumatoid Arthritis (RA) is an autoimmune disease that mainly affects joints in the wrist and hands. It typically results in inflamed and painful joints. MRI is one of the most common imaging modalities to detect and monitor possible inflamed RA-related areas, enabling rheumatologists to treat patients more timely and efficiently. Despite the importance of finding and tracking inflamed areas associated with RA in MRI, there is no previously published work on finding pixel-by-pixel changes related to RA between baseline and follow-up MRIs. Therefore, this paper proposes a hypothesis-free deep learning-based model to discover changes in wrist MRIs on a pixel level to detect changes in inflamed areas related to RA without using prior anatomical information. To do this, a combination of a U-Net-based network and image thresholding was utilised to find pixel-level non-trivial changes between baseline and follow-up MRI images. A wrist MRI dataset including 99 individual pairs of MRI images (each pair constructed of baseline and follow-up images) was used to evaluate the proposed model. Data were collected from patients with clinically suspected arthralgia (CSA), defined as patients at risk of developing RA according to their rheumatologist and already had subclinical inflammation on MRI but could not be diagnosed with RA (yet) since they had not developed clinically detectable arthritis. The obtained results were evaluated using an observer study. The evaluation showed that our proposed model is a promising first step toward developing an automatic model to find RA-related inflammatory changes. Show less
Sbrollini, A.; Barocci, M.; Mancinelli, M.; Paris, M.; Raffaelli, S.; Marcantoni, I.; ... ; Burattini, L. 2023
Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP... Show moreHeart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS & LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF data-base was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P >= 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS & LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable auto-matic HF diagnosis. Show less
Marcantoni, I.; Sbrollini, A.; Morettini, M.; Swenne, C.A.; Burattini, L. 2021
Electrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and Twave alternans (TWA), is an index of cardiac risk. However, only automated TWA measurement... Show moreElectrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and Twave alternans (TWA), is an index of cardiac risk. However, only automated TWA measurement methods have been proposed so far. Here, we presented the enhanced adaptive matched filter (EAMF) method and tested its reliability in both simulated and experimental conditions. Our methodological novelty consists in the introduction of a signal enhancement procedure according to which all sections of the electrocardiogram (ECG) but the wave of interest are set to baseline, and in the extraction of the alternans area (AAr) in addition to the standard alternans amplitude (AAm). Simulated data consisted of 27 simulated ECGs representing all combinations of PWA, QRSA and TWA of low (10 mu V) and high (100 mu V) amplitude. Experimental data consisted of exercise 12-lead ECGs from 266 heart failure patients with an implanted cardioverter defibrillator for primary prevention. EAMF was able to accurately identify and measure all kinds of simulated alternans (absolute maximum error equal to 2%). Moreover, different alternans kinds were simultaneously present in the experimental data and EAMF was able to identify and measure all of them (AAr: 545 mu V x ms, 762 mu V x ms and 1382 mu V x ms; AAm: 5 mu V, 9 mu V and 7 mu V; for PWA, QRSA and TWA, respectively) and to discriminate TWA as the prevalent one (with the highest AAr). EAMF accurately identifies and measures all kinds of electrocardiographic alternans. EAMF may support determination of incremental clinical utility of PWA and QRSA with respect to TWA only. Show less
