Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach Article Swipe
Umile Giuseppe Longo
,
Calogero Di Naro
,
Simona Campisi
,
Carlo Casciaro
,
Benedetta Bandini
,
Ayoosh Pareek
,
Roberta Bruschetta
,
Giovanni Pioggia
,
Antonio Cerasa
,
Gennaro Tartarisco
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13182915
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13182915
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics13182915
- https://www.mdpi.com/2075-4418/13/18/2915/pdf?version=1694439801
- OA Status
- gold
- Cited By
- 6
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386638409
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386638409Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics13182915Digital Object Identifier
- Title
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Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based ApproachWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-11Full publication date if available
- Authors
-
Umile Giuseppe Longo, Calogero Di Naro, Simona Campisi, Carlo Casciaro, Benedetta Bandini, Ayoosh Pareek, Roberta Bruschetta, Giovanni Pioggia, Antonio Cerasa, Gennaro TartariscoList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics13182915Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/13/18/2915/pdf?version=1694439801Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-4418/13/18/2915/pdf?version=1694439801Direct OA link when available
- Concepts
-
Machine learning, Random forest, Artificial intelligence, Support vector machine, Rotator cuff, Medicine, Logistic regression, Algorithm, Naive Bayes classifier, Physical therapy, Computer science, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.45.5% | 201 |
| abstract_inverted_index.46.5% | 170 |
| abstract_inverted_index.48.5% | 195 |
| abstract_inverted_index.Bayes | 131 |
| abstract_inverted_index.Elbow | 104 |
| abstract_inverted_index.Index | 117 |
| abstract_inverted_index.Test, | 93 |
| abstract_inverted_index.after | 28, 57 |
| abstract_inverted_index.could | 257 |
| abstract_inverted_index.data, | 238 |
| abstract_inverted_index.found | 208 |
| abstract_inverted_index.known | 272 |
| abstract_inverted_index.month | 59 |
| abstract_inverted_index.order | 240 |
| abstract_inverted_index.since | 261 |
| abstract_inverted_index.study | 219 |
| abstract_inverted_index.using | 150 |
| abstract_inverted_index.while | 173 |
| abstract_inverted_index.(SVM), | 126 |
| abstract_inverted_index.51.25% | 180 |
| abstract_inverted_index.Across | 141 |
| abstract_inverted_index.Oxford | 108 |
| abstract_inverted_index.Scale, | 99 |
| abstract_inverted_index.Score, | 81, 106, 110 |
| abstract_inverted_index.Simple | 91 |
| abstract_inverted_index.better | 242 |
| abstract_inverted_index.cohort | 256 |
| abstract_inverted_index.forest | 135, 176 |
| abstract_inverted_index.impact | 276 |
| abstract_inverted_index.months | 62, 65 |
| abstract_inverted_index.naïve | 130 |
| abstract_inverted_index.random | 134, 175 |
| abstract_inverted_index.record | 243 |
| abstract_inverted_index.shifts | 245 |
| abstract_inverted_index.vector | 124 |
| abstract_inverted_index.within | 254 |
| abstract_inverted_index.(k-NN), | 129 |
| abstract_inverted_index.36-Item | 87 |
| abstract_inverted_index.Anxiety | 96 |
| abstract_inverted_index.Support | 123 |
| abstract_inverted_index.Survey, | 89 |
| abstract_inverted_index.applied | 35 |
| abstract_inverted_index.certain | 262 |
| abstract_inverted_index.injury, | 270 |
| abstract_inverted_index.machine | 22, 37, 125 |
| abstract_inverted_index.measure | 76 |
| abstract_inverted_index.methods | 229 |
| abstract_inverted_index.metrics | 213 |
| abstract_inverted_index.outcome | 26, 75 |
| abstract_inverted_index.overall | 2 |
| abstract_inverted_index.quality | 253 |
| abstract_inverted_index.repair. | 30 |
| abstract_inverted_index.rotator | 13 |
| abstract_inverted_index.whereas | 82 |
| abstract_inverted_index.American | 101 |
| abstract_inverted_index.Hospital | 95 |
| abstract_inverted_index.Methods: | 33 |
| abstract_inverted_index.Results: | 140 |
| abstract_inverted_index.Shoulder | 80, 92, 102, 109, 113 |
| abstract_inverted_index.Surgeons | 105 |
| abstract_inverted_index.accuracy | 168, 193 |
| abstract_inverted_index.achieved | 165, 179, 200 |
| abstract_inverted_index.applying | 19 |
| abstract_inverted_index.approach | 24 |
| abstract_inverted_index.baseline | 55 |
| abstract_inverted_index.complete | 153 |
| abstract_inverted_index.evaluate | 41 |
| abstract_inverted_index.factors. | 122 |
| abstract_inverted_index.learning | 23, 38 |
| abstract_inverted_index.logistic | 161 |
| abstract_inverted_index.obtained | 190 |
| abstract_inverted_index.outcome, | 47 |
| abstract_inverted_index.outcome. | 279 |
| abstract_inverted_index.patients | 17, 51 |
| abstract_inverted_index.proposal | 6 |
| abstract_inverted_index.shrunken | 155, 185 |
| abstract_inverted_index.smoking, | 266 |
| abstract_inverted_index.surgical | 72 |
| abstract_inverted_index.Materials | 31 |
| abstract_inverted_index.comparing | 210 |
| abstract_inverted_index.diabetes, | 267 |
| abstract_inverted_index.employed. | 139 |
| abstract_inverted_index.evaluated | 53 |
| abstract_inverted_index.extending | 224 |
| abstract_inverted_index.features, | 188 |
| abstract_inverted_index.features. | 158 |
| abstract_inverted_index.k-nearest | 127 |
| abstract_inverted_index.kinematic | 237 |
| abstract_inverted_index.neighbors | 128 |
| abstract_inverted_index.represent | 258 |
| abstract_inverted_index.sessions, | 143 |
| abstract_inverted_index.Depression | 98 |
| abstract_inverted_index.Disability | 116 |
| abstract_inverted_index.Short-Form | 88 |
| abstract_inverted_index.algorithms | 39, 137 |
| abstract_inverted_index.ameliorate | 9 |
| abstract_inverted_index.classifier | 164, 178 |
| abstract_inverted_index.considered | 119 |
| abstract_inverted_index.importance | 222 |
| abstract_inverted_index.innovative | 21 |
| abstract_inverted_index.prediction | 27 |
| abstract_inverted_index.predictive | 121 |
| abstract_inverted_index.predictors | 44 |
| abstract_inverted_index.prognosis. | 249 |
| abstract_inverted_index.regression | 162 |
| abstract_inverted_index.suboptimal | 147 |
| abstract_inverted_index.underlines | 220 |
| abstract_inverted_index.variables, | 263 |
| abstract_inverted_index.algorithms. | 216 |
| abstract_inverted_index.application | 226 |
| abstract_inverted_index.classifiers | 145 |
| abstract_inverted_index.differences | 206 |
| abstract_inverted_index.limitation, | 260 |
| abstract_inverted_index.patients’ | 248 |
| abstract_inverted_index.performance | 148, 212 |
| abstract_inverted_index.predictors, | 232 |
| abstract_inverted_index.significant | 244 |
| abstract_inverted_index.statistical | 205 |
| abstract_inverted_index.Conclusions: | 217 |
| abstract_inverted_index.Limitations: | 250 |
| abstract_inverted_index.arthroscopic | 29 |
| abstract_inverted_index.demonstrated | 146 |
| abstract_inverted_index.neuroimaging | 235 |
| abstract_inverted_index.Specifically, | 159 |
| abstract_inverted_index.intervention. | 73 |
| abstract_inverted_index.Costant–Murley | 79 |
| abstract_inverted_index.state-of-the-art | 36 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5054735571, https://openalex.org/A5080203239 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I155125353, https://openalex.org/I4210130743, https://openalex.org/I4210143387, https://openalex.org/I4210155236 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.87922357 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |