Evidence map›Paper›PMID 42787661›Full record

ArticleSovremennye tekhnologii v meditsine2026

Development of an Artificial Intelligence-Based System for Predicting Fall Risk in Neurological Patients.

E S Ikonnikova, A E Slotina, G A Belyankin, M A Zabello, V Yu Vishnyakov, A V Lobanov, N V Bedeneu, A A Dobrovolsky, D O Zemlyanaya, A Yu Tropynina and 3 more

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

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

E S IkonnikovaJunior Researcher, Laboratory of Neurointerfaces, Institute of Medical Rehabilitation and Restorative Technologies; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
A E SlotinaMD, PhD, Researcher, Institute of Medical Rehabilitation and Restorative Technologies; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
G A BelyankinPhD, Associate Professor, Department of Operations Research, Faculty of Computational Mathematics and Cybernetics; Lomonosov Moscow State University, 1 Leninskie Gory, Moscow, 119991, Russia.
M A ZabelloMaster, Department of Operations Research, Faculty of Computational Mathematics and Cybernetics; Lomonosov Moscow State University, 1 Leninskie Gory, Moscow, 119991, Russia.
V Yu VishnyakovCofounder; LLC Shagometrika, 19, Bldg 2, office 3P, Sadovaya-Spasskaya St., Moscow, 107078, Russia.
A V LobanovCofounder; LLC Shagometrika, 19, Bldg 2, office 3P, Sadovaya-Spasskaya St., Moscow, 107078, Russia.
N V BedeneuResident Physician; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
A A DobrovolskyResident Physician; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
D O ZemlyanayaResident Physician; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
A Yu TropyninaResident Physician; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
O A KirichenkoHead of Rehabilitation Department; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
A V BaidukovaResident Physician; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.
N S SuponevaMD, DSc, Professor, Corresponding Member of Russian Academy of Sciences, Director of Institute of Medical Rehabilitation and Restorative Technologies; Russian Center of Neurology and Neurosciences, 80 Volokolamskoe Shosse, Moscow, 125367, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Materials and Methods: The study involved 187 patients (median age 59 years) with neurological diseases of various etiologies who complained of impaired balance and unsteadiness while walking. All participants underwent video recording of their movements using a smartphone during a 10-meter walk test and Timed Up and Go test. Based on the analysis of 2077 steps recorded in 122 patients, an algorithm for fall risk stratification was developed using the YOLO-NAS Pose M architecture and a two-layer machine learning model. Results: The final model when analyzing video data achieved 76% accuracy in risk prediction, with an average absolute error of time parameter prediction of 1.445 s. The following parameters were identified as key biomechanical predictors of fall risk: step base width at the ankle joint level, lateral trunk sway, step base width at the knee joint level, vertical foot clearance during the swing phase, and temporal gait characteristics.

Indexed as

Accidental FallsArtificial IntelligenceGait Disorders, NeurologicNervous System DiseasesBiomechanical PhenomenaFemaleGaitHumansMaleMiddle AgedPrediction AlgorithmsRisk AssessmentVideo Recordingartificial intelligencebalance disordersfall risk diagnosticsgait disordersmovement biomechanics

Identifiers

PMID42787661
PMCPMC13602023

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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.