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The "ABCD" framework for assessment: a task-oriented approach to reconcile multimodal diagnostic discordance

Wubulikasimu Mijiti1, Shadike Apaer1, Xu Han1

  • 1Department of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.

Frontiers in Medicine
|July 23, 2026
PubMed

Insights

The ABCD framework integrates hiatal hernia (HH) diagnostic tests to reduce uncertainty. This approach aids surgical planning by clarifying discordant findings from endoscopy, barium swallow, and high-resolution manometry (HRM).

Area of Science:

  • Gastroenterology
  • Surgical Diagnostics
  • Medical Imaging Analysis

Background:

  • Pre-operative hiatal hernia (HH) assessment frequently shows discrepancies between endoscopy, barium swallow, and high-resolution manometry (HRM).
  • These diagnostic inconsistencies can result in uncertainty and suboptimal surgical planning for HH.
  • A novel ABCD framework is proposed to integrate multimodal findings for a clinical-task-oriented decision process.

Purpose of the Study:

  • To address diagnostic discordance in pre-operative hiatal hernia (HH) assessment.
  • To propose the ABCD framework for integrating multimodal diagnostic data.
  • To improve clinical decision-making and surgical planning for HH patients.

Main Methods:

  • A comprehensive review of current diagnostic standards (Chicago 4.0, Lyon 2.0, AFS-LDF classification) and emerging technologies like artificial intelligence (AI).
  • Identification of common points of diagnostic discordance and their clinical implications in HH assessment.
  • Development of the ABCD framework to categorize HH assessment into four domains: anatomical, barrier, clinical consequences, and decision support.

Main Results:

  • The ABCD framework categorizes HH assessment into anatomical confirmation, barrier phenotyping, clinical consequences, and decision support.
  • Discordance, such as a large hernia on imaging with a functional barrier on HRM, reflects different dimensions of the esophagogastric junction (EGJ).
  • AI-assisted endoscopic grading shows potential for improved reproducibility, pending external validation.

Conclusions:

  • The ABCD framework provides a structured, task-oriented approach for multimodal HH assessment, aiding patient selection and perioperative decision-making.
  • This framework shifts focus from identifying the 'best test' to addressing multidimensional clinical tasks.
  • Prospective validation is required to confirm the impact of the ABCD framework on clinical outcomes.
Abstract

Keywords:
artificial intelligenceesophagogastric junctionhiatal herniamultimodal imagingsurgical decision-making

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