Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy Article Swipe
Kimji N. Pellano
,
Inga Strümke
,
Daniel Groos
,
Lars Adde
,
Espen A. F. Ihlen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3525571
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3525571
Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. Specifically, we use XAI evaluation metrics — namely faithfulness and stability — to quantitatively assess the reliability of Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in this specific medical application. We utilize a unique dataset of infant movements and apply skeleton data perturbations without distorting the original dynamics of the infant movements. Our CP prediction model utilizes an ensemble approach, so we evaluate the XAI metrics performances for both the overall ensemble and the individual models. Our findings indicate that both XAI methods effectively identify key body points influencing CP predictions and that the explanations are robust against minor data perturbations. Grad-CAM significantly outperforms CAM in the Relative Input Stability velocity (RISv) metric, which measures stability in terms of velocity. In contrast, CAM performs better in the Relative Input Stability bone (RISb) metric, which relates to bone stability, and the Relative Representation Stability (RRS) metric, which assesses internal representation robustness. Individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other, with the ensemble approach providing a representation of outcomes from its constituent models. Both CAM and Grad-CAM also perform significantly better than random attribution, supporting the robustness of these XAI methods. Our work demonstrates that XAI methods can offer reliable and stable explanations for CP prediction models. Future studies should further investigate how the explanations can enhance our understanding of specific movement patterns characterizing healthy and pathological development.
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- en
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https://openalex.org/W4406012401Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3525571Digital Object Identifier
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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral PalsyWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Kimji N. Pellano, Inga Strümke, Daniel Groos, Lars Adde, Espen A. F. IhlenList of authors in order
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https://doi.org/10.1109/access.2025.3525571Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3525571Direct OA link when available
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Computer science, Cerebral palsy, Artificial intelligence, Deep learning, Machine learning, Physical medicine and rehabilitation, MedicineTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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| referenced_works_count | 38 |
| abstract_inverted_index.a | 26, 79, 219 |
| abstract_inverted_index.AI | 22 |
| abstract_inverted_index.CP | 32, 100, 136, 258 |
| abstract_inverted_index.In | 167 |
| abstract_inverted_index.We | 77 |
| abstract_inverted_index.an | 104 |
| abstract_inverted_index.by | 33 |
| abstract_inverted_index.in | 72, 152, 163, 172 |
| abstract_inverted_index.is | 6 |
| abstract_inverted_index.of | 2, 20, 41, 61, 82, 95, 165, 221, 241, 273 |
| abstract_inverted_index.so | 107 |
| abstract_inverted_index.to | 56, 182 |
| abstract_inverted_index.we | 45, 108 |
| abstract_inverted_index.CAM | 151, 169, 207, 228 |
| abstract_inverted_index.Our | 99, 123, 245 |
| abstract_inverted_index.XAI | 47, 111, 128, 243, 249 |
| abstract_inverted_index.and | 11, 18, 53, 66, 85, 119, 138, 185, 205, 229, 254, 279 |
| abstract_inverted_index.are | 142 |
| abstract_inverted_index.can | 251, 269 |
| abstract_inverted_index.for | 8, 114, 257 |
| abstract_inverted_index.how | 266 |
| abstract_inverted_index.its | 224 |
| abstract_inverted_index.key | 132 |
| abstract_inverted_index.nor | 208 |
| abstract_inverted_index.our | 271 |
| abstract_inverted_index.the | 16, 59, 92, 96, 110, 116, 120, 140, 153, 173, 186, 200, 212, 215, 239, 267 |
| abstract_inverted_index.use | 46 |
| abstract_inverted_index.(CP) | 5 |
| abstract_inverted_index.Both | 227 |
| abstract_inverted_index.This | 13 |
| abstract_inverted_index.also | 231 |
| abstract_inverted_index.body | 133 |
| abstract_inverted_index.bone | 177, 183 |
| abstract_inverted_index.both | 115, 127 |
| abstract_inverted_index.data | 36, 88, 146 |
| abstract_inverted_index.deep | 27 |
| abstract_inverted_index.from | 38, 223 |
| abstract_inverted_index.show | 202 |
| abstract_inverted_index.than | 235 |
| abstract_inverted_index.that | 30, 126, 139, 248 |
| abstract_inverted_index.this | 73 |
| abstract_inverted_index.with | 214 |
| abstract_inverted_index.work | 246 |
| abstract_inverted_index.(CAM) | 65 |
| abstract_inverted_index.(RRS) | 190 |
| abstract_inverted_index.(XAI) | 23 |
| abstract_inverted_index.Class | 62, 68 |
| abstract_inverted_index.Early | 0 |
| abstract_inverted_index.Input | 155, 175 |
| abstract_inverted_index.Palsy | 4 |
| abstract_inverted_index.apply | 86 |
| abstract_inverted_index.minor | 145 |
| abstract_inverted_index.model | 102 |
| abstract_inverted_index.offer | 252 |
| abstract_inverted_index.paper | 14 |
| abstract_inverted_index.terms | 164 |
| abstract_inverted_index.tests | 15 |
| abstract_inverted_index.these | 242 |
| abstract_inverted_index.using | 25 |
| abstract_inverted_index.video | 39 |
| abstract_inverted_index.which | 160, 180, 192 |
| abstract_inverted_index.(RISb) | 178 |
| abstract_inverted_index.(RISv) | 158 |
| abstract_inverted_index.Future | 261 |
| abstract_inverted_index.assess | 58 |
| abstract_inverted_index.better | 171, 234 |
| abstract_inverted_index.infant | 42, 83, 97 |
| abstract_inverted_index.method | 29 |
| abstract_inverted_index.models | 198 |
| abstract_inverted_index.namely | 51 |
| abstract_inverted_index.other, | 213 |
| abstract_inverted_index.points | 134 |
| abstract_inverted_index.random | 236 |
| abstract_inverted_index.robust | 143 |
| abstract_inverted_index.should | 263 |
| abstract_inverted_index.stable | 255 |
| abstract_inverted_index.unique | 80 |
| abstract_inverted_index.varied | 203 |
| abstract_inverted_index.within | 199 |
| abstract_inverted_index.Mapping | 64, 70 |
| abstract_inverted_index.against | 144 |
| abstract_inverted_index.crucial | 7 |
| abstract_inverted_index.dataset | 81 |
| abstract_inverted_index.enhance | 270 |
| abstract_inverted_index.further | 264 |
| abstract_inverted_index.healthy | 278 |
| abstract_inverted_index.medical | 75 |
| abstract_inverted_index.methods | 24, 129, 250 |
| abstract_inverted_index.metric, | 159, 179, 191 |
| abstract_inverted_index.metrics | 49, 112 |
| abstract_inverted_index.models. | 122, 226, 260 |
| abstract_inverted_index.neither | 206 |
| abstract_inverted_index.overall | 117 |
| abstract_inverted_index.perform | 232 |
| abstract_inverted_index.relates | 181 |
| abstract_inverted_index.studies | 262 |
| abstract_inverted_index.utilize | 78 |
| abstract_inverted_index.without | 90 |
| abstract_inverted_index.— | 50, 55 |
| abstract_inverted_index.Cerebral | 3 |
| abstract_inverted_index.Grad-CAM | 148, 209, 230 |
| abstract_inverted_index.Relative | 154, 174, 187 |
| abstract_inverted_index.approach | 217 |
| abstract_inverted_index.assesses | 193 |
| abstract_inverted_index.dynamics | 94 |
| abstract_inverted_index.ensemble | 105, 118, 201, 216 |
| abstract_inverted_index.evaluate | 109 |
| abstract_inverted_index.findings | 124 |
| abstract_inverted_index.identify | 131 |
| abstract_inverted_index.indicate | 125 |
| abstract_inverted_index.internal | 194 |
| abstract_inverted_index.learning | 28 |
| abstract_inverted_index.measures | 161 |
| abstract_inverted_index.methods. | 244 |
| abstract_inverted_index.movement | 275 |
| abstract_inverted_index.original | 93 |
| abstract_inverted_index.outcomes | 222 |
| abstract_inverted_index.patterns | 276 |
| abstract_inverted_index.performs | 170 |
| abstract_inverted_index.predicts | 31 |
| abstract_inverted_index.reliable | 253 |
| abstract_inverted_index.results, | 204 |
| abstract_inverted_index.skeletal | 35 |
| abstract_inverted_index.skeleton | 87 |
| abstract_inverted_index.specific | 74, 274 |
| abstract_inverted_index.utilizes | 103 |
| abstract_inverted_index.velocity | 157 |
| abstract_inverted_index.Stability | 156, 176, 189 |
| abstract_inverted_index.analyzing | 34 |
| abstract_inverted_index.approach, | 106 |
| abstract_inverted_index.contrast, | 168 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.effective | 9 |
| abstract_inverted_index.extracted | 37 |
| abstract_inverted_index.movements | 84 |
| abstract_inverted_index.providing | 218 |
| abstract_inverted_index.stability | 54, 162 |
| abstract_inverted_index.velocity. | 166 |
| abstract_inverted_index.(Grad-CAM) | 71 |
| abstract_inverted_index.Activation | 63, 69 |
| abstract_inverted_index.Individual | 197 |
| abstract_inverted_index.distorting | 91 |
| abstract_inverted_index.evaluation | 48 |
| abstract_inverted_index.individual | 121 |
| abstract_inverted_index.movements. | 43, 98 |
| abstract_inverted_index.outperform | 211 |
| abstract_inverted_index.prediction | 101, 259 |
| abstract_inverted_index.recordings | 40 |
| abstract_inverted_index.robustness | 240 |
| abstract_inverted_index.stability, | 184 |
| abstract_inverted_index.supporting | 238 |
| abstract_inverted_index.Explainable | 21 |
| abstract_inverted_index.constituent | 225 |
| abstract_inverted_index.effectively | 130 |
| abstract_inverted_index.influencing | 135 |
| abstract_inverted_index.investigate | 265 |
| abstract_inverted_index.monitoring. | 12 |
| abstract_inverted_index.outperforms | 150 |
| abstract_inverted_index.predictions | 137 |
| abstract_inverted_index.reliability | 17, 60 |
| abstract_inverted_index.robustness. | 196 |
| abstract_inverted_index.application. | 76 |
| abstract_inverted_index.attribution, | 237 |
| abstract_inverted_index.consistently | 210 |
| abstract_inverted_index.demonstrates | 247 |
| abstract_inverted_index.development. | 281 |
| abstract_inverted_index.explanations | 141, 256, 268 |
| abstract_inverted_index.faithfulness | 52 |
| abstract_inverted_index.intervention | 10 |
| abstract_inverted_index.pathological | 280 |
| abstract_inverted_index.performances | 113 |
| abstract_inverted_index.Specifically, | 44 |
| abstract_inverted_index.applicability | 19 |
| abstract_inverted_index.perturbations | 89 |
| abstract_inverted_index.significantly | 149, 233 |
| abstract_inverted_index.understanding | 272 |
| abstract_inverted_index.Representation | 188 |
| abstract_inverted_index.characterizing | 277 |
| abstract_inverted_index.perturbations. | 147 |
| abstract_inverted_index.quantitatively | 57 |
| abstract_inverted_index.representation | 195, 220 |
| abstract_inverted_index.Gradient-weighted | 67 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.92304531 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |