Evidence map›Paper›PMID 39107349›Full record

ArticleScientific reports2024

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Minah Kim, Jungha Lee, Soo Yong Lee, Minji Ha, Inkyung Park, Jiseon Jang, Moonyoung Jang, Sunghyun Park, Jun Soo Kwon

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Minah Kim *Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Jungha Lee *Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
Soo Yong LeeKim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea.
Minji HaDepartment of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
Inkyung ParkDepartment of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
Jiseon JangDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Moonyoung JangDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Sunghyun ParkDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Jun Soo KwonDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea. kwonjs@snu.ac.kr.

Funding

Korea Brain Research Institute 21-BR-03-01National Research Foundation of Korea 2020M3E5D9079910National Research Foundation of Korea RS-2023-00266120
6 · The paper itself

Abstract

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to detect impaired executive function during visuospatial memory encoding in patients with mental illnesses. In 96 patients with first-episode psychosis, 49 with clinical high risk for psychosis, 104 with OCD, and 159 healthy controls, eye movements were recorded during a 3-min RCFT figure memorization task, and organization and immediate recall scores were obtained. These scores, along with the fixation points indicating eye-focused locations in the figure, were used to train a Long Short-Term Memory + Attention model for detecting impaired executive function and visuospatial memory. The model distinguished between normal and impaired executive function, with an F

Indexed as

Deep LearningExecutive FunctionEye-Tracking TechnologyAdolescentAdultAttentionEye MovementsFemaleHumansMaleMental DisordersNeural Networks, ComputerNeuropsychological TestsObsessive-Compulsive DisorderPsychotic DisordersYoung AdultDeep learningEarly psychosisExecutive functionEye trackingObsessive‒compulsive disorderVisuospatial memory

Identifiers

PMID39107349
PMCPMC11303748

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.