Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Abstract

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.

Artificial intelligence and machine learning in pain research: a data scientometric analysis

“…Nevertheless, data needs are a limitation of machine learning, and the impact of using algorithms trained with patient data in clinical practice on patient care is receiving increasing attention among which the concept of "explainable AI" 1 as one approach to the latter has been already applied in pain research. 16,54 3. Limitations of machine learning methods…”

Effects of Unilateral Muscle Fatigue on Thermographic Skin Surface Temperature of Back and Abdominal Muscles—A Pilot Study

“…This may also highlight the potential for the use of thermography data in the context of differential diagnosis for being able to detect muscular imbalances. In the context of asymmetric fatigue, the additional consideration of the spine through analysis of the vertebral orientations (see for example [ 32 ]) may add additional insights and should be considered in future works. Furthermore, in order to be able to make a statement about the monitoring ability of the thermography and physical load, it seems interesting to check these results with EMG.…”

Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

“…A new soft magnetic sensor array system was successfully fabricated to achieve intervertebral load monitoring of robotically actuated human spine replica, promising the potential to preview artificial disc implant suitability in a patient-specific fashion. Results showed that the soft magnetic sensor array system has the high capability to classify five different postures of the spine, which can be a predictor of different problems of the spine that people experience [ 30 ]. In comparing the two soft magnets on the algorithms’ abilities to detect different loads and locations, the soft magnet with 15% ferrofluid generally produced higher classification accuracy in detecting the different loads applied to each taxel ( Table S1, Figure S1 ).…”