A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Murdaca, Giuseppe; Caprioli, Simone; Tonacci, Alessandro; Billeci, Lucia; Greco, Mónica; Negrini, Simone; Cittadini, Giuseppe; Zentilin, Patrizia; Spagnolo, Elvira Ventura; Gangemi, Sebastiano

doi:10.3390/diagnostics11101880

Cited by 14 publications

(24 citation statements)

References 37 publications

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“…DL architectures have shown excellent performance in the medical and commercial fields [ 21 , 22 , 23 , 24 , 25 ]. Therefore, DL is primarily employed in the detection of COVID-19 infection and drug repurposing in diverse ways [ 26 , 27 , 28 , 29 ]. Several researchers have employed CNN to speed up the analysis of COVID-19 infected images [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Khan¹,

Sohail²,

Khan

2022

Diagnostics

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“…Those results with explainable AI can also help physicians achieve early prevention, as well as identify the WS severity level to screen SSc severities in a very short time. In this way, it will be possible to combine clinical and instrumental data to assist immuno-rheumatologists to predict early lung involvement for early diagnosis in SSc [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Computer-Aided Diagnosis of Rheumatoid Arthritis with Hand X-ray Images Conforming to Modified Total Sharp/van der Heijde Score

Wang

Lai

et al. 2022

Biomedicines

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Introduction: Rheumatoid arthritis (RA) is a systemic autoimmune disease; early diagnosis and treatment are crucial for its management. Currently, the modified total Sharp score (mTSS) is widely used as a scoring system for RA. The standard screening process for assessing mTSS is tedious and time-consuming. Therefore, developing an efficient mTSS automatic localization and classification system is of urgent need for RA diagnosis. Current research mostly focuses on the classification of finger joints. Due to the insufficient detection ability of the carpal part, these methods cannot cover all the diagnostic needs of mTSS. Method: We propose not only an automatic label system leveraging the You Only Look Once (YOLO) model to detect the regions of joints of the two hands in hand X-ray images for preprocessing of joint space narrowing in mTSS, but also a joint classification model depending on the severity of the mTSS-based disease. In the image processing of the data, the window level is used to simulate the processing method of the clinician, the training data of the different carpal and finger bones of human vision are separated and integrated, and the resolution is increased or decreased to observe the changes in the accuracy of the model. Results: Integrated data proved to be beneficial. The mean average precision of the proposed model in joint detection of joint space narrowing reached 0.92, and the precision, recall, and F1 score all reached 0.94 to 0.95. For the joint classification, the average accuracy was 0.88, and the accuracy of severe, mild, and healthy reached 0.91, 0.79, and 0.9, respectively. Conclusions: The proposed model is feasible and efficient. It could be helpful for subsequent research on computer-aided diagnosis in RA. We suggest that applying the one-hand X-ray imaging protocol can improve the accuracy of mTSS classification model in determining mild disease if it is used in clinical practice.

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“…Systemic sclerosis (SSc) is an autoimmune disease that affects multiple organ systems, including the lungs [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Interstitial lung disease (ILD) is a common manifestation in systemic sclerosis (SSc) and, despite continuous advances in treatment, remains the major cause of death in SSc patients [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Interstitial lung disease (ILD) is a common manifestation in systemic sclerosis (SSc) and, despite continuous advances in treatment, remains the major cause of death in SSc patients [ 1 , 2 , 3 , 4 ]. It is of utmost importance to detect ILD in its very early stages so as to be able to choose the correct treatment regimen [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Imaging plays a central role in the management of systemic sclerosis-associated interstitial lung disease (SSc-ILD), and chest high-resolution computed tomography (HRCT) is currently considered the ‘gold standard’ for establishing an ILD diagnosis [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Computed Tomography and Lung Ultrasound in Patients with Systemic Sclerosis: Which One to Choose?

et al. 2021

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Imaging plays a pivotal role in systemic sclerosis for both diagnosis management of pulmonary complications, and high-resolution computed tomography (HRCT) is the most sensitive technique for the evaluation of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Indeed, several studies have demonstrated that HRCT helps radiologists and clinicians to make a correct diagnosis on the basis of recognised typical patterns for SSc-ILD. Most SSc patients affected by ILD have a non-specific interstitial pneumonia pattern (NISP) on HRCT scan, whilst a minority of cases fulfil the criteria for usual interstitial pneumonia (UIP). Moreover, several recent studies have demonstrated that lung ultrasound (LUS) is an emergent tool in SSc diagnosis and follow-up, although its role is still to be confirmed. Therefore, this article aims at evaluating the role of LUS in SSc screening, aimed at limiting the use of CT to selected cases.

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A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Cited by 14 publications

References 37 publications

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Deep Learning-Based Computer-Aided Diagnosis of Rheumatoid Arthritis with Hand X-ray Images Conforming to Modified Total Sharp/van der Heijde Score

High-Resolution Computed Tomography and Lung Ultrasound in Patients with Systemic Sclerosis: Which One to Choose?

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