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Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach
Aim: The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. Materials and Methods: We applied state-of-the-art machine learning algorithms to evaluate the best...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530213/ https://www.ncbi.nlm.nih.gov/pubmed/37761282 http://dx.doi.org/10.3390/diagnostics13182915 |
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author | Longo, Umile Giuseppe Di Naro, Calogero Campisi, Simona Casciaro, Carlo Bandini, Benedetta Pareek, Ayoosh Bruschetta, Roberta Pioggia, Giovanni Cerasa, Antonio Tartarisco, Gennaro |
author_facet | Longo, Umile Giuseppe Di Naro, Calogero Campisi, Simona Casciaro, Carlo Bandini, Benedetta Pareek, Ayoosh Bruschetta, Roberta Pioggia, Giovanni Cerasa, Antonio Tartarisco, Gennaro |
author_sort | Longo, Umile Giuseppe |
collection | PubMed |
description | Aim: The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. Materials and Methods: We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant–Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. Results: Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. Conclusions: This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients’ prognosis. Limitations: The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome. |
format | Online Article Text |
id | pubmed-10530213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105302132023-09-28 Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach Longo, Umile Giuseppe Di Naro, Calogero Campisi, Simona Casciaro, Carlo Bandini, Benedetta Pareek, Ayoosh Bruschetta, Roberta Pioggia, Giovanni Cerasa, Antonio Tartarisco, Gennaro Diagnostics (Basel) Article Aim: The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. Materials and Methods: We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant–Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. Results: Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. Conclusions: This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients’ prognosis. Limitations: The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome. MDPI 2023-09-11 /pmc/articles/PMC10530213/ /pubmed/37761282 http://dx.doi.org/10.3390/diagnostics13182915 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Longo, Umile Giuseppe Di Naro, Calogero Campisi, Simona Casciaro, Carlo Bandini, Benedetta Pareek, Ayoosh Bruschetta, Roberta Pioggia, Giovanni Cerasa, Antonio Tartarisco, Gennaro Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title | Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title_full | Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title_fullStr | Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title_full_unstemmed | Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title_short | Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach |
title_sort | application of machine learning algorithms for prognostic assessment in rotator cuff pathologies: a clinical data-based approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530213/ https://www.ncbi.nlm.nih.gov/pubmed/37761282 http://dx.doi.org/10.3390/diagnostics13182915 |
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