Evidence map›Paper›PMID 41657739›Full record

ReviewFrontiers in surgery2025

Robotics total knee arthroplasty: is an image-based the only solution?

Alessandro El Motassime, Lorenzo Fulli, Luca Andriollo, Corrado Ciatti, Rudy Sangaletti, Francesco Benazzo, Stefano Marco Paolo Rossi

Abstract readReview
In one paragraph

Review in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Alessandro El MotassimeOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.
Lorenzo FulliOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.
Luca AndriolloOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.
Corrado CiattiDipartimento di Ortopedia e Traumatologia, Ospedale Guglielmo da Saliceto, Piacenza, Italy.
Rudy SangalettiOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.
Francesco BenazzoOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.
Stefano Marco Paolo RossiOrtopedia e Traumatologia, Fondazione Poliambulanza, Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic-assisted total knee arthroplasty (TKA) significantly enhances surgical precision and alignment accuracy. While traditional robotic systems rely on preoperative imaging, imageless technology has emerged as a viable alternative, leading to a reduction in costs, radiation exposure, and logistical challenges. This narrative review aims to evaluate the efficacy and reliability of imageless robotic-assisted TKA, specifically assessing its accuracy in component positioning, functional outcomes, and potential advantages over image-based systems. A review of current literature was conducted, comparing imageless robotic TKA with both manual and image-based techniques. The key parameters analyzed include alignment precision, joint line restoration, patient-reported outcomes, and complication rates. Notably, imageless robotic-assisted TKA demonstrated alignment accuracy and functional outcomes comparable to those of image-based systems, while providing enhancements in workflow efficiency and the elimination of radiation exposure. Although a moderate learning curve was observed, no significant differences in patient satisfaction or clinical performance were recorded. Overall, imageless robotic-assisted TKA represents a safe and effective alternative to image-based systems, achieving comparable surgical precision with additional economic and practical benefits. Further research is required to confirm long-term outcomes and to optimize intraoperative guidance strategies.

Indexed as

arthroplastyimageless TKAkneerobotTKA

Identifiers

PMID41657739
PMCPMC12872902

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.