IA Model for Acute Appendicitis in CT

NCT06175169 | Unknown DMC

• 1 other identifier: Project MHo4
• Study Type: interventional
• Target: 568
• Locations: 1 country
• Sites: 1

Brief Summary

the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a clinician does for deciding treatment. The dataset was built from a large population of patients visiting emergency departments who underwent intravenous contrast-enhanced abdominopelvic CT examinations to evaluate abdominal pain in the right or lower quadrant area as the chief complaint. Recently, the IA model was externally validated using a dataset of multicenter institutions through data exfiltration. In this study, the investigators hypothesized that the IA model would show a comparable negative appendicitis rate of \<10% non-inferior margins compared to non-radiologists with a shorter interpretation time in a prospectively randomized dataset.

Conditions

Acute Appendicitis

Keywords

deep learning

Outcome Measures

Primary Outcomes (1)

• Negative appendectomy rate

  Description: False positive rate = FP /FP +TN FP: false positive, TN true negative Assessment of outcomes: To evaluate diagnostic performance in humans, four test items for non-radiologists were set up as follows: (1) Appendix visualization: Can you find the location of the appendix? ① Yes ② No (2) Exclusion of appendicitis: Can appendicitis be excluded from the CT images? ① Yes (non-appendicitis) ② No (simple or complicated appendicitis) (3) If your choice is "no (simple or complicated appendicitis)," is appendicitis accompanied by complication? ① Yes (complicated appendicitis) ② No (simple appendicitis) (4) Finally, what is the radiologic diagnosis based on CT images in patients presenting with acute right or lower abdominal pain in the ER? ① Non-appendicitis ② simple appendicitis ③ complicated appendicitis The NAR was calculated as the primary endpoint using the outcomes of (2) the answer sheet of the non-radiologist and the Stage 1 IA model-yielding class.

  Outcome measurement for four test items of non-radiologists will be assessed for an average of one year through study completion.

Study Arms (2)

IA model

ACTIVE COMPARATOR

A fully automated diagnostic framework based on 3D-CNN model to predict non-appendicitis, simple and complicated appendicitis

Device: Information of Appendix (IA) model

Non-radiologist

NO INTERVENTION

Ten non-radiologists participated in this study. CT image same to IA model was allocated to radiologist randomly.

Interventions

Information of Appendix (IA) model DEVICE

Deep learning model

IA model

Eligibility Criteria

• Age: 12 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• When the imaging protocol parameters were as follows: abdomen or pelvis (intravenous contrast, 2 mg/kg, maximum 160 mL), scan timing (portal venous phase), range (from 4 cm above the liver dome to 1 cm below the ischial tuberosity), radiation dose (tube potential, KVP from 100 to 120), pitch 1.75:1, and reconstruction (5 mm, cut slice for adults; 3 mm, cut slice for children under 12 years old), anonymized CT images of patients were referred to a randomized dataset.

You may not qualify if:

• Patients who did not fulfill the CT imaging protocol were excluded in detail as follows:
• i) Failure to meet the CT protocol criteria of this study: liver CT, biliary CT, etc. (if contrast phase was different); ureter CT, etc. (if contrast media was not used and the reconstruction method was different); non-enhanced CT (when contrast media was not used); and appendix CT or low-dose CT (when radiation dose was low).
• ii) When the quality of the CT image is significantly reduced, as follows: when blurring occurs (motion artifact) or metal artifact (when internal fixation is performed due to spinal surgery).
• iii) when it was evident from the medical record review that clinical information suggested that APCT was performed due to the suspicion of a condition other than appendicitis, as follows: suspected acute cholecystitis due to RUQ tenderness and Murphy's sign; suspected urolithiasis due to flank pain and gross hematuria; suspected pancreatitis due to a history of pancreatitis; alcohol abuse; and suspected gynecological diseases due to vaginal discharge. Suspected panperitonitis due to whole abdominal tenderness, rebound tenderness, and unstable vital signs. Patients with acute cholecystitis, ureteral stones, pancreatitis, or acute peritonitis due to small bowel or colon perforation were also excluded.
• v) diagnosed by ultrasound sonography vi) Patients who were transferred to the emergency department after a diagnosis of appendicitis at an outside hospital or ambulatory care were excluded.
• vii) Patients with appendicitis who did not undergo surgical treatment because of the enrollment protocol of other ongoing studies.
• viii) patients who had undergone an appendectomy

Sponsors & Collaborators

• Hallym University Medical Center: lead
• Doheon Institute for Digital Innovation In Medicine: collaborator
• Medical A.I. Center of Hallym University: collaborator

Study Sites (1)

Hallym University Medical Center

Anyang-si, Gyeonggi-do, 14068, South Korea

RECRUITING

Related Links

• Medical A.I. Center of Hallym University

MeSH Terms

Conditions

Appendicitis

Interventions

Models, Biological

Condition Hierarchy (Ancestors)

Intraabdominal Infections; Infections; Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Cecal Diseases; Intestinal Diseases

Intervention Hierarchy (Ancestors)

Models, Theoretical; Investigative Techniques

Study Officials

• Iltae Son

  Hallym University Medical Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Iltae Son

CONTACT

+82-10-2373-4783; 1tae99@hanmail.net

Minsung Kim

CONTACT

+82-10-2087-9792; bongkay4@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: All image data were anonymized with the deletion of Dicom header information such as sex, age, and CT protocol. Participants were given CT slices that were identical to the range of the VOI for appendicitis generated automatically through the extraction pipeline of the IA model. A total of 20 axial CT slice images of a 5-mm cut were masked with blinded truth labeling and a blackened background on the outside of the body surface.
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: A deep learning model, "Information of Appendix (IA)," based on a fully automated 3D convolutional neural network using transfer learning, has been developed to diagnose appendicitis in an abdominopelvic CT scan. The IA model was trained, validated, and tested on 3D-DenseNet, 3D-EfficientNet, and 3D-ResNet. External validation was completed through the data exfiltration of external institutions. The IA model yielded the probability of three classifications: non-appendicitis, simple appendicitis, and complicated appendicitis for patients who presented to the emergency room with abdominal pain and underwent abdominopelvic CT for suspected acute appendicitis. In this study, we confirmed that the intervention did not affect the included patients. Considering the minimal risk to human subjects and the ethical and legal aspects of artificial intelligence, we excluded the involvement of the IA model in all treatment processes.

Study Record Dates

• First Submitted: November 30, 2023
• First Posted: December 18, 2023
• Study Start: July 4, 2023
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2025
• Last Updated: December 18, 2023

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)