@Article{Sédiri2026,
journal="Biology of Sport",
issn="0860-021X",
year="2026",
title="Digital twins in sports science: applications for performance 
enhancement, injury prevention, and rehabilitation through 
advanced big data analytics and deep learning methodologies  – a comprehensive narrative review",
abstract="Digital	twin	(DT)	technology,	combined	with	advanced	computational	methodologies,	represents	a paradigm	shift	in	sports	science.	DTs	generate	virtual	athlete	replicas	through	real-time	data	integration	and	predictive	analytics.	While	computational	capabilities	and	data	acquisition	have	advanced	rapidly,	DT	applications	in	sports	remain	fragmented,	warranting	systematic	synthesis.	This	narrative	review	examines	the	applications	of	DT	in	sports,	with	a focus	on	big	data	analytics	and	deep	learning	for	performance	enhancement,	injury	prevention,	and	rehabilitation.	A narrative	review	was	conducted	using	publications	from	2018 to	2025 across	multiple	databases.	Eligible	studies	applied	DT	frameworks,	Artificial	Intelligence	(AI),	including	machine	learning	algorithms	and	deep	neural	networks,	or	big	data	analytics	in	elite	and	amateur	sport	contexts,	with	a focus	on	football	as	a case	study.	Extracted	data	focused	on	technological	approaches,	clinical	outcomes,	and	practical	 applications.	DT	applications	cluster	into	three	domains:	(1)	performance	enhancement	via	biomechanical	modelling	(convolutional	or	recurrent	neural	networks);	(2)	injury	prevention	using	ensemble	learning	and	predictive	risk	models;	and	(3)	rehabilitation	optimization	through	multimodal	sensors	and	virtual	reality.	Key	examples	include	cycling	telemetry,	computer	vision	for	technique	correction,	and	real-time	musculoskeletal	monitoring.	The	integration	of	generative	AI	and	the	Internet	of	Things	further	enhances	predictive	accuracy	and	decision-making.	DTs	offer	significant	potential	for	proactive	athlete	management.	Widespread	adoption	requires	standardized	protocols,	clinical	validation,	and	robust	ethical	frameworks	for	data	privacy.	Successful	integration	supports	data-driven	training,	individualized	recovery,	and	enhanced	athlete	welfare.",
author="Sédiri, Afef
and Barbaria, Sabri
and Ceylan, Halil
and De Giorgio, Andrea
and Puce, Luca
and Stefanica, Valentina
and Bragazzi, Nicola
and Dergaa, Ismail
and Rahmouni, Hanene",
pages="1271--1291",
doi="10.5114/biolsport.2026.161106",
url="http://dx.doi.org/10.5114/biolsport.2026.161106"
}