Development of a spontaneous pain indicator based on brain cellular calcium using deep learning

Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extensive efforts have been made in preclinical studies, there still exists a mismatch in pain type between the animal model and humans (i.e., evoked vs. spontaneous), which obstructs the translation of knowledge from preclinical animal models into objective diagnosis and effective new treatments. Here, we developed a deep learning algorithm, designated AI-bRNN (Average training, Individual test-bidirectional Recurrent Neural Network), to detect spontaneous pain information from brain cellular Ca2+ activity recorded by two-photon microscopy imaging in awake, head-fixed mice. AI-bRNN robustly determines the intensity and time points of spontaneous pain even in chronic pain models and evaluates the efficacy of analgesics in real time. Furthermore, AI-bRNN can be applied to various cell types (neurons and glia), brain areas (cerebral cortex and cerebellum) and forms of somatosensory input (itch and pain), proving its versatile performance. These results suggest that our approach offers a clinically relevant, quantitative, real-time preclinical evaluation platform for pain medicine, thereby accelerating the development of new methods for diagnosing and treating human patients with chronic pain. Pain: AI-based platform could aid analgesic drug discovery A microscopy technique coupled with an artificial intelligence (AI) platform could help researchers discover new types of pain-relief medicines. A team from South Korea led by Sun Kwang Kim of Kyung Hee University and Sang Jeong Kim of Seoul National University created a machine-learning algorithm that converts calcium signaling data in the brain, as estimated via imaging on genetically engineered mice, into a measurement of pain intensity. The researchers applied the technique to several mouse models of chronic pain and showed that it accurately captured the analgesic effects of known painkillers. They also extended the system to multiple brain regions, cell types and another brain-controlled sensory process, itch. The researchers propose using the AI-based tool to evaluate candidate anti-pain and anti-itch medicines ahead of human trials.

Automated summary

Researchers have developed a deep learning algorithm that can detect spontaneous pain information and evaluate the effectiveness of analgesics based on brain cellular activity. The algorithm performs well in chronic pain models and can be applied to various cell types, brain areas, and forms of somatosensory input.