automated analysis of patient response after chiropractic interventions through machine learning

Project Title

automated analysis of patient response after chiropractic interventions through machine learning

Project Area of Specialization Artificial IntelligenceProject Summary

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Low-back pain (LBP) is the leading cause of disability worldwide1 and is associated with annual economic costs up to AU $9.2 billion2 and US $102 billion3 in Australia and the United States of America, respectively. In addition to economic burden, multiple individual factors (e.g. loss of social identity4, distress5 and physical deconditioning6) contribute to pain intensity and disability in this population group7. Approximately 90 percent of people with LBP are classified as having ‘non-specific’ LBP, where no clear tissue cause of pain can be found8. However, we anticipate that people with non-specific LBP are not a homogeneous group, yet the challenge remains to identify potential sub-groups that could benefit from specific treatments to assist in reducing the burden of the condition

Project Objectives

machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ?5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test?retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

Project Implementation Method

Low back Pain (LBP) is the main source of incapacity worldwide and a significant reason for work non-attendance in the dynamic populace. As a repetitive condition, avoidance is critical. Home activities are viable, yet adherence and precise execution of the activities are hard to screen by specialists and advisors. AI (ML) applied to restoration frameworks could be an answer for address telerehabilitation for individuals with ongoing LBP if it holds adequate exactness in checking adherence execution while giving patient direction. The point was to look and audit concentrates on that have utilized ML methods for recovery of individuals with LBP. To foster a comprehension on the results estimated, the clinical setting (up close and personal recovery or far off restoration) where intercessions occurred, and the clinical examination strategy that has been utilized. 

Benefits of the Project

Our objective is to use software technology and techniques for the betterment of mankind. Using the data given and techniques studied, we can reduce the pain faced by humans. By applying the proper algorithm and best possible techniques, we can achieve our aim. We will read the Patient Responses and extract our needed data. Using this data, we can focus on the different techniques used by chiropractors and get the best possible technique for pain. Using all this, we would have a ground truth model that will automate our process. 

Technical Details of Final Deliverable

This project has a scope in the health sector because our world needs it. It allows us to save humans from pain. It also checks the temperature of the person. This is the big system in the world because the main problem which we are facing is increased due to touching each other, we are building a system which gives an advantage this problem and using this system, we can decrease the rate of people who are suffering from pain. This project solves many issues regarding patients and to be used in almost every place because of its so much used around the world. 

Final Deliverable of the Project Software SystemCore Industry MedicalOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	40000
nvidia 1060 ti	Equipment	1	40000	40000