Multimodal sensing improves detection of pain responses in dental tests
A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation
Machine Learning
Summary
Pain responses during dental tests can be hard to measure because pain is personal and varies a lot. This paper shows that signals from the body's autonomic nervous system, like heart rate and skin activity, can help doctors understand how patients react to dental stimulation without just asking them. The authors combined different body signals using a machine learning model to tell when a patient feels no, mild, or intense pain during a cold test on a tooth nerve. Their approach was fairly accurate and could help dentists assess patient responses more objectively in the future.
What this means in practice
- •For dental clinicians: Enable more objective assessment of patient pain responses during nerve testing by integrating autonomic signals with existing procedures.
- •For medical device designers: Design non-invasive devices that use combined autonomic signals to improve detection of pain intensity in clinical diagnostics.
Authors
Youngsun Kong, Yubin Choi, Dongjin Song, Dong-Guk Shin, I-Ping Chen, Ki Chon
Abstract
Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodontic diagnosis. However, pain is a subjective sensory and emotional experience that varies considerably across individuals and can be difficult to communicate. We investigated whether complementary autonomic signals could support objective assessment of responses during dental examination. Forty-nine patients underwent cold pulp testing, yielding no-response, mild-response, and intense-response conditions. The framework integrated ECG-derived skin nerve activity (SKNA) and R-R intervals (RRI), together with electrodermal activity (EDA), using temporal convolutional network encoders with attention-based mid-level fusion. Individual baseline signals and subject-level covariates, including anxiety scores and biological sex, were also incorporated. The framework achieved 80.2% balanced accuracy, 75.2% sensitivity, and 85.2% specificity for binary classification of no response versus mild or intense response. For three-class classification, it achieved 60.0% balanced accuracy and a 58.8% macro-averaged F1 score. Ablation and attention-weight analyses indicated that EDA contributed most strongly to model performance, followed by RRI, while SKNA improved balanced accuracy by approximately five percentage points. Age was significantly associated with model performance. These findings support the feasibility of multimodal autonomic sensing for objective, non-invasive assessment of responses to dental pulp stimulation.