Evidence map›Paper›PMID 42609746›Full record

ArticleFrontiers in medicine2026

Prospective validation protocol for artificial intelligence-assisted standardization of Coma Recovery Scale-Revised scores using electroencephalography and clinical data in disorders of consciousness.

Chuangan Zhou, Yuzhu Gao, Jinqiu Shan, Yida Dai, Yanfei Zhuang, Dazhong Sun, Xiangfeng Chi

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Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Chuangan Zhou *Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Yuzhu Gao *Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Jinqiu Shan *The Fifth Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Yida DaiGuangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Yanfei ZhuangGuangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Dazhong SunGuangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Xiangfeng ChiGuangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Disorders of consciousness (DoC) after severe acquired brain injury are difficult to diagnose at the bedside. The Coma Recovery Scale-Revised (CRS-R) is the behavioral reference standard, but raw scores can be affected by arousal fluctuation, motor impairment, sensory deficits, sedation, language disturbance, and local assessment conditions. Objective: This protocol describes a prospective validation study of a multimodal artificial intelligence (AI) system designed to standardize CRS-R scores by integrating routine electroencephalography (EEG), bedside CRS-R subscale scores, and selected clinical contextual variables against an adjudicated standardized reference. Methods and analysis: The study includes retrospective model development followed by prospective, blinded clinical validation at Guangdong Second Traditional Chinese Medicine Hospital. The locked model will use subscale-specific ordinal prediction modules and a rule-constrained integration layer to generate AI-standardized CRS-R subscale scores, total scores, diagnostic categories, uncertainty estimates, and prespecified case-level explanations. Prospective participants will undergo two independent bedside CRS-R assessments and routine scalp EEG within target and maximum assessment windows. A blinded expert committee will generate the reference CRS-R score using paired behavioral records, available videos, nursing observations, medication and confounder records. The primary endpoint is the participant-level reduction in absolute CRS-R total-score error after AI-assisted standardization relative to the adjudicated reference. Secondary endpoints include subscale agreement, diagnostic-category agreement, inter-rater variability, uncertainty-flag performance, workflow feasibility, and exploratory day-14 consciousness improvement. Ethics and dissemination: The protocol was approved by the Ethics Committee of Guangdong Second Traditional Chinese Medicine Hospital (approval number Z202603-004-01; review date March 25, 2026; approval validity March 27, 2026 to March 26, 2027). Results will be disseminated through peer-reviewed publication and academic presentations. Clinical Trial Registration: https://www.chictr.org.cn, Chinese Clinical Trial Registry, ChiCTR2600124984.

Indexed as

artificial intelligenceclinical validationComa Recovery Scale-Reviseddisorders of consciousnesselectroencephalographySPIRIT-AIstudy protocol

Identifiers

PMID42609746
PMCPMC13478918

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.