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NCT Number: NCT07841327

Artificial Intelligence Versus Anesthesiologist Visual Assessment of Ejection Fraction by Transesophageal Echocardiography During Cardiac Surgery

This prospective observational study evaluated the reliability of a general-purpose artificial intelligence application in estimating left ventricular ejection fraction from intraoperative transesophageal echocardiography images during cardiac surgery. The artificial intelligence estimates were compared with visual assessment by two independent blinded professor anesthesiologists and with preoperative transthoracic echocardiography findings. Ejection fraction measured using M-mode and the time required for each assessment were also evaluated. The study included 93 adults undergoing elective coronary artery bypass grafting or valve surgery. The artificial intelligence assessments were performed using pseudonymized images and were not used to guide clinical decisions or patient management.

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Key information

Age range

21 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Cardiothoracic Surgery Academy, Ain Shams University Hospitals

Cairo, Cairo Governorate, Egypt

About this study

This was a prospective, single-cohort observational study conducted at the Cardiothoracic Surgery Academy, Ain Shams University. Adults aged 21 years or older undergoing elective coronary artery bypass grafting or valve repair or replacement surgery were enrolled consecutively after providing written informed consent.

After induction of anesthesia and hemodynamic stabilization, transesophageal echocardiography images were obtained using a transgastric short-axis view at the mid-papillary level and an angle of 0 degrees. End-diastolic and end-systolic frames and an M-mode recording were collected for assessment of left ventricular ejection fraction.

Pseudonymized echocardiographic images were assessed using a general-purpose artificial intelligence application. The same images were independently assessed by two blinded professor anesthesiologists who had no access to the artificial intelligence output or the other assessments. Visual estimation of ejection fraction, M-mode-derived ejection fraction, and interpretation time were recorded. These findings were also compared with the preoperative transthoracic echocardiography assessment.

The artificial intelligence output was used exclusively for research and was not used to make or modify any clinical decision. A total of 93 eligible participants were included in the final analysis.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age 21 years or older.
  • Scheduled for elective cardiac surgery involving coronary artery bypass grafting, valve repair, or valve replacement.
  • Availability of a preoperative transthoracic echocardiographic assessment.
  • Either sex.
  • American Society of Anesthesiologists physical status III or IV.

Exclusion criteria

  • Coagulopathy, defined as an international normalized ratio greater than 2 or a platelet count below 50,000/mm3.
  • Previous cardiac surgery (redo procedure).
  • Esophageal or upper gastrointestinal pathology preventing safe transesophageal echocardiography probe placement, including esophageal stricture, tumor, or large varices.
  • Poor-quality transesophageal echocardiography images despite standard optimization, preventing reliable interpretation.
  • Severe arrhythmia or hemodynamic instability before surgery, during induction, or during surgery that could compromise the accuracy of the transesophageal echocardiography assessment.
  • Refusal to participate or a condition limiting valid consent or participation, including cognitive impairment or a language barrier.

Treatment and study plan

Artificial Intelligence Assessment of TEE Images

Diagnostic Test

Pseudonymized end-diastolic and end-systolic frames and an M-mode recording obtained from the transgastric short-axis mid-papillary view at 0 degrees were analyzed by a general-purpose artificial intelligence application to estimate left ventricular ejection fraction. The artificial intelligence results were compared with assessments by two independent blinded professor anesthesiologists and with preoperative transthoracic echocardiography. The artificial intelligence output was used only for research and did not guide clinical management.

Primary outcomes

  1. Difference Between AI and Anesthesiologist Visual Assessment of LVEF

    Time frame: During cardiac surgery, at the time of the intraoperative TEE assessment

    Left ventricular ejection fraction was assessed from the same intraoperative transesophageal echocardiography transgastric short-axis mid-papillary view at 0 degrees by a general-purpose artificial intelligence application and by two independent blinded professor anesthesiologists using visual (eyeball) assessment. The paired assessments were compared to evaluate the difference and agreement between the artificial intelligence and anesthesiologist assessments.

Secondary outcomes

  1. Difference Between AI and Anesthesiologist M-Mode Assessment of LVEF

    Time frame: During cardiac surgery, at the time of the intraoperative TEE assessment

    Left ventricular ejection fraction was assessed using M-mode in the intraoperative transesophageal echocardiography transgastric short-axis mid-papillary view at 0 degrees by a general-purpose artificial intelligence application and by two independent blinded professor anesthesiologists. The paired M-mode ejection fraction assessments were compared to evaluate the difference and agreement between methods.

  2. Time Required for AI and Anesthesiologist LVEF Assessment

    Time frame: During intraoperative TEE image review, from the start of each visual (eyeball) or M-mode LVEF assessment until the corresponding estimate is completed, assessed up to 3 minutes per assessment per participant.

    The time required for the general-purpose artificial intelligence application and the anesthesiologist assessors to complete the visual (eyeball) and M-mode left ventricular ejection fraction assessments was recorded separately using a timer and reported in seconds. Assessment times were compared between the artificial intelligence and anesthesiologist methods.

  3. Difference Between Intraoperative TEE and Preoperative TTE LVEF Assessments

    Time frame: From the available preoperative TTE assessment to the intraoperative TEE assessment during cardiac surgery

    Left ventricular ejection fraction estimates obtained from intraoperative transesophageal echocardiography by the general-purpose artificial intelligence application and the blinded anesthesiologist assessors were compared with the available preoperative transthoracic echocardiography ejection fraction for the same participant. The difference between each intraoperative assessment and the preoperative transthoracic echocardiography value was evaluated.

Interested in participating?

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Sponsors and collaborators

Lead sponsor

Ain Shams University

Other

Registry information

Official study title

Reliability of Artificial Intelligent General Application in Assessing Ejection Fraction in Comparison to Anesthesiologist Eye Ball by Trans Oesophageal Echo During Cardiac Surgery

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Sep 25, 2026
Registry last updated
Sep 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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