Artificial Intelligence-Driven Chronic Pain Management with Mobile App Integration for Empowering Primary Healthcare Physicians

Rationale: Chronic pain affects 20-30% of the population, often untreated due to a lack of specialists. This project aims to bridge this gap by developing a mobile app integrated with artificial intelligence (AI), empowering primary care physicians in diagnosing and managing chronic pain.

Study Design:

Pilot Study.

Inclusion Criteria:

Patients with chronic pain diseases will be enrolled to prepare clinical feature databank and pilot testing of the app; chronic pain diseases that will be included are listed at the end of proposal

Exclusion Criteria:

Patient Refusal

Patients with following red flag conditions:

Malignancy

Motor deficit or progressive neurological deficit

Osteoporosis

Trauma

Infection

Immunosuppression

Objectives:

Develop an AI-based mobile app capable of diagnosing and managing chronic-pain diseases.

Evaluate the feasibility of chronic-pain management by primary care physicians using the AI-based app.

Establish a clinical feature databank of prevalent chronic-pain conditions in India to support the app’s algorithm.

Methods: A multidisciplinary team will develop an AI-based mobile app for chronic-pain management, utilizing advanced deep-learning and machine-learning techniques. Pain specialists will categorize conditions and use clinical data to train the algorithm. Advanced deep-learning models like CNNs, transformers, attention models and RNNs will extract patterns. Pilot studies will assess primary care physicians’ ability to manage chronic-pain using the app, supported by training modules. The project aims to deliver an AI-powered app and promote AI adoption in medical sciences, particularly in Uttar Pradesh, with accompanying AI lab facilities.

Expected Outcomes:

Development of an AI-based mobile-app for diagnosing and managing chronic-pain diseases.

Establishment of AI lab facilities at SGPGIMS, Lucknow.

Creation of training modules to raise awareness of AI’s application in medical sciences, particularly in Uttar Pradesh.

Outcome Measures:

Primary Outcome Measure: Probability of provision of correct diagnosis and management by app

Secondary Outcome measures: Primary care physician satisfaction

Sample size estimation and sampling strategy:

Based on the data reported at different centres, about 20-30% patients are diagnosed with chronic pain among the individuals seeking treatment for the pain. Taking the incidence of 20%, at minimum two-sided 95% confidence interval and 20% relative error in the reported incidence, required at least 385 patients. Considering the possible data loss and to increase the accuracy of the findings, 500 subjects will be included in the study. Power analysis and sample size version-16 (PASS-16, NCSS) was used for the sample size estimation.

For the validation of app, further 100 consecutive eligible patients will be enrolled.

Chronic Pain Diseases:

Head and Neck

Migraine

Tension headache

Trigeminal autonomic cephalalgias

Occipital Neuralgia

Neck:

Cervical facet pain

Myofascial pain

Cervical disc prolapse

Cervical spondylitis

Face:

Trigeminal Neuralgia

Atypical Facial pain

Trigeminal neuropathy

Post-herpetic neuralgia

Glossopharyngeal neuralgia

Upper Back:

Myofascial pain

Vertebral compression fracture

Nerve entrapment pain

Thoracic facet joint pain

Chest

Chest wall pain

Nerve entrapment pain

Myofascial pain

Post-herpetic neuralgia

Lower Back

Facet joint pain

Sacro-iliac joint pain

Myofascial pain

Vertebral compression fracture

Lumbar disc prolapse

Abdomen

Antero-cutaneous nerve entrapment pain

Myofascial pain

Scar neuralgia

Bladder pain syndrome

Upper limb

Frozen shoulder

Cervical disc prolapse

Cervical facet joint pain

Myofascial pain

Sympathetic pain

Lower limb:

Lumbar disc prolapse

umbar facet joint pain

Myofascial pain

Sympathetic pain

Joint Pain (Knee, shoulder and other joints)

Osteoarthritis

Rheumatoid arthritis

Ankylosing spondylitis