Can machine learning models support physicians in systemic lupus erythematosus diagnosis? Results from a monocentric cohort

Ceccarelli, Fulvia; Lapucci, Matteo; Olivieri, G.; Sortino, Alessio; Natalucci, Francesco; Spinelli, Francesca Romana; Alessandri, Cristiano; Sciandrone, Marco; Conti, Fabrizio

doi:10.1016/j.jbspin.2021.105292

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Eleven hundred and sixty-six (1166) ineligible articles were excluded after browsing the abstracts and titles, and full texts of the remaining sixty (60) articles were read. Finally, a total of eighteen (18) studies were included, in which ten (10) [15][16][17][18][19][20][21][22][23][24] focused on SLE and the remaining eight (8) [25][26][27][28][29][30][31][32] on NPSLE. Te study selection process is shown in Figure 1, and the characteristics of included studies are shown in Tables 1 (for SLE) and 2 (for NPSLE).…”

Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Background. Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support. Methods. Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated. Results. Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25. Conclusion. Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

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Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…Similarly, another study aimed to enable earlier diagnoses of lupus or alert clinicians to ‘red flags’ that may suggest SLE [12]. Following the initial identification of 58 features, similar to those examined by Adamichou et al [10 ▪▪ ], the authors evaluated the classification performance of three different machine learning models based on a final 12 features.…”

Section: Machine Learning In the Diagnosis Of Lupusmentioning

confidence: 99%

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

Kingsmore

Lipsky

2022

Current Opinion in Rheumatology

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Purpose of reviewMachine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. Recent findingsThe frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

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“…Machine learning algorithms are potential tools that could be integrated into the diagnostic process for the evaluation and detection of rheumatic diseases [3,[11][12][13][14][15][16][17][18][19][20][21][22]. These algorithms have become important components in the diagnosis of conditions due to their inherent ability to analyze large datasets to identify trends and patterns that are not easily visible in the datasets and use the information to make predictions with high precision.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models

Mills,

Dey,

Kassim

et al. 2024

BioMedInformatics

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Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.

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Can machine learning models support physicians in systemic lupus erythematosus diagnosis? Results from a monocentric cohort

Cited by 6 publications

References 9 publications

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models

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