An estimated one in five Americans live with chronic pain and current treatment options leave much to be desired. Feixiong Cheng, PhD, Director of Cleveland Clinic’s Genome Center, and IBM are using artificial intelligence (AI) for drug discovery in advanced pain management. The team’s deep-learning framework identified multiple gut microbiome-derived metabolites and FDA-approved drugs that can be repurposed to select non-addictive, non-opioid options to treat chronic pain.

The findings, published in Cell Press, represent one of many ways the organizations’ Discovery Accelerator partnership is helping to advance research in healthcare and life sciences.

Treating chronic pain with opioids is still a challenge due to the risk of severe side effects and dependency, says co-first author Yunguang Qiu, PhD, a postdoctoral fellow in Dr. Cheng’s lab whose research program focuses on developing therapeutics for nervous system disorders. Recent evidence has shown that drugging a specific subset of pain receptors in a protein class called G protein-coupled receptors (GPCRs) can provide non-addictive, non-opioid pain relief. The question is how to target those receptors, Dr. Qiu explains.

Instead of inventing new molecules from scratch, the team wondered whether they could apply research methods they had already developed for finding preexisting FDA-approved drugs for potential pain indication. Part of this process involves mapping out gut metabolites to spot drug targets.

To identify these molecules, the first author and computational scientist Yuxin Yang, PhD, a former Kent State University graduate student. Dr. Yang completed his thesis research in Dr. Cheng’s lab and continues to work there as a data scientist. Drs. Yang and Qiu led a team to update a previous drug discovery AI algorithm the Cheng Lab had developed. Collaborators from IBM helped write and edit the manuscript.

“Our IBM collaborators gave us valuable advice and perspective to develop advanced computational techniques,” Dr. Yang says. “I’m happy for the opportunity to work with and learn from peers in the industry sector.”

To determine whether a molecule will work as a drug, researchers need to predict how it will physically interact with and influence proteins in our body (in this case, our pain receptors). To do this, the researchers need a 3D understanding of both molecules based on extensive 2D data about their physical, structural and chemical properties.

“Even with the help of current computational methods, combining the amount of data we need for our predictive analyses is extremely complex and time-consuming,” Dr. Cheng explains. “AI can rapidly make full use of both compound and protein data gained from imaging, evolutionary and chemical experiments to predict which compound has the best chance of influencing our pain receptors in the right way.”

The research team’s tool, called LISA-CPI (Ligand Image- and receptor’s three-dimensional (3D) Structures-Aware framework to predict Compound-Protein Interactions) uses a form of artificial intelligence called deep learning to predict:

The team used LISA-CPI to predict how 369 gut microbial metabolites and 2,308 FDA- approved drugs would interact with 13 pain-associated receptors. The AI framework identified several compounds that could be repurposed to treat pain. Studies are underway to validate these compounds in the lab.

“This algorithm’s predictions can lessen the experimental burden researchers must overcome to even come up with a list of candidate drugs for further testing,” Dr. Yang says. “We can use this tool to test even more drugs, metabolites, GPCRs and other receptors to find therapeutics that treat diseases beyond pain, like Alzheimer’s disease.”

Dr. Cheng added that this is just one example of how the team is collaborating with IBM to develop small molecule foundation models for drug development – including both drug repurposing in this study and an ongoing novel drug discovery project.

“We believe that these foundation models will offer powerful AI technologies to rapidly develop therapeutics for multiple challenging human health issues,” he says.

Study Info:

This is a collaborative project with Dr. Qiang Guan from Kent State University, and Dr. Jianying Hu and Dr. Michal Rosen-Zvi from IBM.

/Public Release. This material from the originating organization/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here.
 

Step into the Future: How AI-Driven Calculators are Cracking the Code on Pain Research

Step into the Future: How AI-Driven Calculators are Cracking the Code on Pain Research

AI-driven calculators are revolutionizing pain research by enabling real-time data analysis, patient-specific pain management, and predictive modeling. These advanced tools combine machine learning algorithms with patient-reported data to provide a deeper understanding of pain patterns, ultimately helping healthcare professionals deliver more personalized, effective care.

Usage:

AI-driven pain calculators can be used in various healthcare settings, particularly in telehealth, to:

  • Track and monitor pain intensity, location, and duration in real-time.
  • Analyze patient data to uncover pain patterns and triggers.
  • Provide actionable insights to physicians for customized treatment plans.
  • Enhance pain assessments for chronic conditions such as arthritis, migraines, or fibromyalgia.
  • Support clinical research by aggregating data for large-scale pain studies.

Pros:

  1. Personalized Treatment: AI calculators help tailor pain management strategies to each patient’s unique experience, increasing treatment effectiveness.
  2. Real-time Analysis: Data is processed instantly, providing immediate feedback for both patients and healthcare professionals.
  3. Consistency: Standardized data collection ensures more accurate and reliable assessments over time.
  4. Improved Patient Engagement: By tracking their pain regularly, patients become more involved in managing their condition.
  5. Predictive Capabilities: AI algorithms can predict future pain episodes based on historical data, helping patients and doctors prepare in advance.

Cons:

  1. Data Privacy Concerns: Storing sensitive pain-related data in AI systems can be vulnerable to privacy breaches if not securely handled.
  2. Over-reliance on Technology: Physicians may place too much trust in AI-generated insights, potentially overlooking human intuition or subjective patient experiences.
  3. Limited Understanding of Complex Pain: AI may struggle with nuances in pain perception, such as emotional or psychological pain triggers, limiting the scope of its analysis.
  4. Cost: Implementing AI systems in clinical settings can be expensive, making it inaccessible to smaller healthcare providers.

Limitations:

  1. Data Quality Dependency: AI-driven calculators rely heavily on accurate and consistent data input. Poor data quality or incomplete reporting can skew results.
  2. Lack of Human Sensitivity: AI systems may lack the human empathy needed to fully understand the psychological aspects of chronic pain.
  3. Generalizability: Current AI models are often trained on specific datasets, which might not generalize well to diverse populations with different pain experiences.
  4. Regulatory Hurdles: Integrating AI-driven tools into healthcare systems may face regulatory barriers that slow down adoption, especially in regions with strict health data laws.

Conclusion:

While AI-driven pain calculators offer significant advantages in revolutionizing pain research and personalized care, they should be used alongside traditional clinical judgment. As AI continues to evolve, these tools will likely become even more sophisticated, contributing to better pain management solutions in the future. Example below is not for diagnosis or treatment rather a demo on how AI can personalize pain care journals which can be an important tool in continuation of major pain research break through.

AI-Driven Pain Calculator

AI-Driven Pain Calculator

Pain diagram front Pain diagram back

FAQs – AI-Driven Calculators in Pain Research

FAQs for “Step into the Future: How AI-Driven Calculators are Cracking the Code on Pain Research”

1. What are AI-driven pain calculators?

AI-driven pain calculators are advanced tools that use artificial intelligence and machine learning to analyze patient-reported pain data. These tools help identify patterns, track pain intensity and duration, and assist healthcare providers in making informed treatment decisions.

2. How do AI-driven calculators assist in pain research?

AI tools analyze large datasets, including patient pain diaries, real-time input, and biological data, to uncover trends in pain experiences. This helps researchers understand pain triggers, develop predictive models, and create more targeted therapies based on data-driven insights.

3. Can AI calculators be used during telehealth visits?

Yes! AI-driven pain calculators are highly beneficial in telehealth settings. Patients can enter data regarding pain intensity, location, and duration, allowing healthcare providers to track progress, assess treatments, and adjust care remotely and in real time.

4. How does the AI predict pain episodes?

By analyzing historical patient data, including pain onset, intensity patterns, and external factors (e.g., weather, activity levels), AI algorithms can predict when pain episodes are likely to occur. This allows both patients and doctors to anticipate and manage pain proactively.

5. What are the benefits of using AI-driven calculators in pain management?

The benefits include:

  • Personalized treatment plans tailored to individual pain experiences.
  • Real-time analysis of pain data for immediate feedback.
  • Improved patient engagement in tracking and managing pain.
  • Consistency and accuracy in pain reporting.
  • Predictive capabilities that help manage future pain episodes.

6. Are there any privacy concerns with AI-driven pain calculators?

Yes, data privacy is a critical concern. Pain-related data is highly sensitive, and any AI system that stores or analyzes this information must adhere to strict privacy regulations such as HIPAA in the U.S. to ensure patient confidentiality and data protection.

7. Can AI calculators understand all types of pain?

While AI-driven calculators are excellent at tracking physical pain based on data input, they may struggle with the emotional and psychological dimensions of pain. These tools are best used in conjunction with human insights for a comprehensive understanding of complex pain experiences.

8. What are some limitations of AI in pain research?

Some limitations include:

  • Dependence on high-quality data input for accurate analysis.
  • Inability to fully capture emotional and psychological pain factors.
  • Generalization challenges when applying AI models to diverse populations.
  • High costs and regulatory hurdles in implementing AI tools in healthcare systems.

9. Can AI-driven calculators replace traditional pain assessment methods?

No, AI-driven calculators are designed to complement traditional pain assessment methods, not replace them. Human clinicians still play a crucial role in interpreting pain data, understanding patient context, and providing empathetic care.

10. What is the future of AI-driven pain calculators in healthcare?

As AI continues to evolve, these tools will become more sophisticated, offering even more accurate pain predictions and personalized treatment plans. In the future, AI-driven calculators may be integrated with wearable technology to provide continuous pain monitoring and more dynamic, responsive healthcare solutions.

Glossary – AI-Driven Pain Calculators

Glossary: AI-Driven Pain Calculators

Artificial Intelligence (AI)
AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI is used in pain calculators to process patient data and generate insights.
Machine Learning (ML)
A subset of AI, machine learning involves algorithms that allow computers to learn from data and improve their performance over time without being explicitly programmed. In pain research, ML helps identify patterns in pain data.
Telehealth
Telehealth refers to the delivery of healthcare services through digital communication technologies, allowing patients to consult with healthcare professionals remotely. AI-driven pain calculators are often used in telehealth consultations to track and assess pain.
Pain Management
Pain management is the medical discipline that focuses on the prevention, diagnosis, and treatment of pain. AI-driven calculators assist in optimizing personalized pain management strategies.
Data Privacy
Data privacy refers to the protection of personal data, ensuring that patient information is kept secure. AI systems processing pain data must comply with regulations like HIPAA to safeguard patient confidentiality.
HIPAA
The Health Insurance Portability and Accountability Act (HIPAA) is a U.S. law designed to provide privacy standards to protect patients’ medical records and other health information. AI systems used in healthcare must follow HIPAA regulations.
Chronic Pain
Chronic pain is long-term pain that lasts beyond the normal healing time. It can be continuous or intermittent and is often challenging to manage. AI-driven tools help track chronic pain and provide insights for personalized treatment.
Predictive Analytics
Predictive analytics uses statistical techniques, machine learning, and data mining to analyze historical data and predict future outcomes. AI-driven pain calculators use predictive analytics to anticipate future pain episodes.
Wearable Technology
Wearable technology refers to electronic devices that can be worn on the body, such as fitness trackers or health monitors. In the context of pain management, AI-driven calculators may be integrated with wearables to continuously monitor pain levels.
Big Data
Big data refers to large and complex data sets that are difficult to analyze using traditional methods. AI-driven calculators analyze big data from multiple patients to identify trends in pain experiences and improve treatment strategies.
Real-Time Analysis
Real-time analysis refers to the immediate processing of data as it is inputted, providing instant feedback. AI calculators offer real-time pain assessments, allowing for faster adjustments to treatment plans.
Empathy in Healthcare
Empathy is the ability to understand and share the feelings of another person. While AI-driven tools can provide data insights, they cannot fully replace the human touch and empathy needed in patient care.

Note: This glossary covers common terms related to AI-driven pain calculators, helping you better understand their impact on pain research and healthcare.

Resources – AI-Driven Pain Calculators

Resources: AI-Driven Pain Calculators & Pain Research

Here are some valuable resources to learn more about AI in healthcare, pain research, and related technologies.

Note: These resources provide a mix of research papers, regulatory guidelines, and educational materials to deepen your understanding of AI’s role in pain research and management.

Disclaimer: AI-Driven Pain Calculators

Disclaimer: AI-Driven Pain Calculators and Research Information

Important Notice: The information provided in the article “Step into the Future: How AI-Driven Calculators are Cracking the Code on Pain Research” and the associated AI-Driven Pain Calculator is for educational and informational purposes only.

Please be aware of the following:

  • The AI-Driven Pain Calculator is a demonstration tool and should not be used for medical diagnosis or treatment decisions.
  • The information and results provided by this tool are not a substitute for professional medical advice, diagnosis, or treatment.
  • Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.
  • Never disregard professional medical advice or delay in seeking it because of something you have read in this article or calculated using this tool.
  • The creators and publishers of this content are not responsible for any errors or omissions in the information provided.
  • By using the AI-Driven Pain Calculator, you acknowledge and agree that you understand these terms and assume all risk associated with the use of this information.

In case of a medical emergency, call your local emergency services immediately.

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