Journal of plastic, reconstructive & aesthetic surgery : JPRAS · 2025
Machine learning and computer vision for detection and classification of pain in lower extremity amputees.
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Rao Arya S, Raasveld Floris V, Moussa Omar, Johnston Benjamin R, Luan Anna, Zhang Zihe, Woolf Clifford J, Tuaño Krystle R, Valerio Ian L, Eberlin Kyle R
Аннотация
OBJECTIVE: Pain sketches help visualize neuropathic pain patterns in amputees and may predict surgical outcomes following targeted muscle reinnervation (TMR). Current manual interpretation introduces subjectivity and potential bias. Machine learning offers potential for automated, objective classification of these sketches. Therefore, we aimed to develop and evaluate a machine learning approach for automated classification of pain sketches from lower extremity amputees who underwent targeted muscle reinnervation (TMR).
METHODS: Here, 588 pain sketches from 206 lower extremity amputees (2021-2024) were analyzed. Convolutional neural networks were trained to perform binary classifications between pain categories (focal, radiating, diffuse, and no pain). Unsupervised hierarchical clustering was used to identify novel pain distribution patterns.
RESULTS: Binary classification models achieved the highest performance distinguishing no pain versus diffuse pain (AUROC: 0.799). Other models showed AUROCs between 0.587-0.760. Hierarchical clustering revealed distinct pain distribution patterns based on anatomical location and extent, providing insights beyond traditional classification schemes.
CONCLUSIONS: Machine learning can effectively automate pain sketch classification in lower extremity amputees, offering potential clinical utility for preoperative planning. This approach may help standardize interpretation and improve surgical decision-making for TMR procedures.
LEVEL OF EVIDENCE: IV-Therapeutic.
METHODS: Here, 588 pain sketches from 206 lower extremity amputees (2021-2024) were analyzed. Convolutional neural networks were trained to perform binary classifications between pain categories (focal, radiating, diffuse, and no pain). Unsupervised hierarchical clustering was used to identify novel pain distribution patterns.
RESULTS: Binary classification models achieved the highest performance distinguishing no pain versus diffuse pain (AUROC: 0.799). Other models showed AUROCs between 0.587-0.760. Hierarchical clustering revealed distinct pain distribution patterns based on anatomical location and extent, providing insights beyond traditional classification schemes.
CONCLUSIONS: Machine learning can effectively automate pain sketch classification in lower extremity amputees, offering potential clinical utility for preoperative planning. This approach may help standardize interpretation and improve surgical decision-making for TMR procedures.
LEVEL OF EVIDENCE: IV-Therapeutic.