Feasibility study of rehabilitation for cardiac patients aided by an artificial intelligence web-based programme: a randomised controlled trial (RECAP trial)—a study protocol

Witharana, Pasan; Chang, Lisa; Maier, Rebecca; Ogundimu, Emmanuel; Wilkinson, Christopher; Athanasiou, Thanos; Akowuah, Enoch

doi:10.1136/bmjopen-2023-079404

Cited by 2 publications

(1 citation statement)

References 53 publications

(90 reference statements)

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“…The predictive model utilizing the ANN is both reasonable and accurate in examining the impact on minimal clinically successful therapy in tendinopathy treated with ESWT [ 22 ]. In the field of physical medicine and rehabilitation, various machine-learning approaches have been developed in recent years across several subfields of the discipline [ 23 , 24 , 25 , 26 ]. Therefore, evaluating an ANN may be a solution to ensure a better and more accurate prognosis, integrating medical and physical examination and also including RM score assessment to evaluate patient pain compared to usual daily life activities [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach

Santilli,

Vetrano,

Mangone

et al. 2024

Life

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The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient’s quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant–Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient’s initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.

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