Effectiveness of Artificial Intelligence Algorithms

January 20, 2026 updated by: Ramazan Baldemir, Ankara Ataturk Sanatorium Training and Research Hospital

Effectiveness of Artificial Intelligence Algorithms in Predicting Postoperative Pain in Lung Resections

Introduction Throughout human history, surgical interventions have been frequently used in human treatment. However, despite their therapeutic properties, the pain experienced by patients, especially in the acute postoperative period, can be quite challenging for clinicians. Acute postoperative pain is an important public health issue. While 80% of patients report experiencing pain in the postoperative period, 88% of them experience moderate or higher levels of pain. According to another study, more than 60% of surgical patients suffer from moderate to severe acute postoperative pain, and this pain has been associated with the development of chronic postoperative pain. Poorly managed postoperative pain can lead to negative outcomes such as lower patient satisfaction, delayed patient recovery, increased length of hospital stay, increased care costs, chronic pain, unnecessary opioid prescription, opioid abuse, overdose, and death. In addition, in order to provide effective pain management, the method of providing preventive analgesic treatment before the pain begins is frequently used. However, this situation may lead to unnecessary medication administration in many patients and consequently, many adverse events such as bleeding, respiratory depression, cardiac events or gastrointestinal system side effects of opioids, nonsteroidal anti-inflammatory drugs and other analgesics. As a result, the difficulty in predicting acute postoperative pain leads to suboptimal pain management. Therefore, being able to predict which patients will suffer from moderate to severe acute postoperative pain will optimize the risk-benefit ratio of perioperative analgesic treatments and ensure that appropriate treatment is given. Although different studies on this subject have tried to predict postoperative pain with logistic regression analysis, the desired result has not yet been achieved. This situation becomes even more important in surgeries with a high risk of severe pain in the postoperative period, such as lung resection. In order to reduce or prevent postoperative pulmonary complications in patients undergoing lung resection, it is very important for patients to be able to cough without feeling pain and thus to remove secretions from the respiratory tract. If sufficient analgesia is not provided, these patients cannot perform this effectively. This increases complications, hospital stay, and patient care costs. In order to prevent these negative situations and provide optimal analgesia, new methods are needed to predict postoperative pain levels. Numerous models have been proposed in studies to understand the risk factors that will exacerbate severe acute postoperative pain. Most of the research in this area has focused on determining risk factors for postoperative pain using statistical methodology. Previous studies suggest that machine learning models can outperform linear statistical models in classifying postoperative pain-related outcomes when similar features are considered. Therefore, artificial intelligence (AI) algorithms are algorithms that can combine and analyze complex data with hundreds of variables and provide new outputs, and can guide an effective solution in predicting and managing the postoperative process. Previous studies have shown promising results in predicting acute postoperative pain with an area under the curve (AUC) of 0.70 using artificial intelligence algorithms to predict pain with perioperative data. However, studies on this topic are needed in a specific surgery such as lung resection, which has the potential for severe pain.

This study aimed to predict postoperative pain by analyzing perioperative data using AI algorithms in lung resections and to determine the effectiveness of AI algorithms in this regard. Thus, it aimed to reduce unnecessary analgesic use in patients, eliminate possible side effects of these drugs, and start effective analgesic treatment in a timely manner in patients with high pain risk.

Purpose/Hypothesis:

This study aimed to predict postoperative pain by analyzing perioperative data using AI algorithms in lung resections and to determine the effectiveness of AI algorithms in this regard.

H0: Artificial intelligence algorithms are not effective in predicting postoperative pain in lung resections.

H1: Artificial intelligence algorithms are effective in predicting postoperative pain in lung resections.

Material-Method:

This study will be conducted in accordance with the Declaration of Helsinki and will be carried out at the SBÜ Ankara Atatürk Sanatorium Training and Research Hospital after receiving ethics committee approval. Our study is a retro-prospective study. Retrospectively collected patient data will be evaluated prospectively with artificial intelligence algorithms.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

601

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Turkey (Türkiye)
    • Keçiören
      • Ankara, Keçiören, Turkey (Türkiye), 06290
        • ANKARA ATATURK SANATORİUM TRAİNİNG AND RESEARCH HOSPITAL

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients who underwent lung resection surgery between January 2023 and October 2024 and have a complete pain follow-up form will be included in our retro-prospective cross-sectional study.

Description

Inclusion Criteria:

  • Age > 18,
  • Patients who underwent lung resection between January 2023 and October 2024 will be included.

Exclusion Criteria:

  • Patients under 18 years of age
  • Patients with missing postoperative pain form
  • Patients who did not undergo lung resection

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Lung Resection
patients who underwent lung resection between January 2023 and October 2024
Other: Artificial Intelligence
Ollama (Ollama., 2024, https://ollama.ai/) artificial intelligence program and "PYTHON 3 Programming Language" and open source libraries will be used for the necessary algorithms for data review and analysis. In case of deficiencies in the data of the patients; the missing data will be edited using "Data Imputation" techniques. Number of files to be reviewed and/or date range to be covered: Patients who underwent lung resection between January 2023 and October 2024 will be included. An estimated 2000 files are planned to be scanned.
Other Names:
  • AI

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Numerical Rating Scale (NRS)
Time Frame: 24 hour
NRS is the verbal or written determination of the pain level on a scale between 0 and 10; where 0 represents no pain and 10 represents unbearable pain. NRS 1-3; mild pain, NRS 4-6; moderate pain, NRS 7-10; severe pain will be defined.
24 hour

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ankara Ataturk Sanatorium Training and Research Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 8, 2025

Primary Completion (Actual)

July 15, 2025

Study Completion (Actual)

January 20, 2026

Study Registration Dates

First Submitted

January 8, 2025

First Submitted That Met QC Criteria

January 8, 2025

First Posted (Actual)

January 13, 2025

Study Record Updates

Last Update Posted (Actual)

January 22, 2026

Last Update Submitted That Met QC Criteria

January 20, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2024-BÇEK/177

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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