Evidence map›Paper›PMID 39338224›Full record

ReviewJournal of personalized medicine2024

Minimally Invasive Therapies for Knee Osteoarthritis.

Uchenna Osuala, Megan H Goh, Arian Mansur, John B Smirniotopoulos, Arielle Scott, Christine Vassell, Bardia Yousefi, Neil K Jain, Alan A Sag, Allison Lax and 8 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. 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

18 authors.

Uchenna OsualaGeorgetown University School of Medicine, Washington, DC 20007, USA.ORCID 0000-0002-6490-2682
Megan H GohHarvard Medical School, Cambridge, MA 02115, USA.ORCID 0009-0002-8278-4656
Arian MansurHarvard Medical School, Cambridge, MA 02115, USA.ORCID 0000-0003-2406-5127
John B SmirniotopoulosGeorgetown University School of Medicine, Washington, DC 20007, USA.ORCID 0000-0001-5786-8351
Arielle ScottDepartment of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA.ORCID 0009-0002-2022-8558
Christine VassellDepartment of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA.
Bardia YousefiDepartment of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA.ORCID 0000-0003-3121-4573
Neil K JainDivision of Vascular and Interventional Radiology, MedStar Washington Hospital Center, Washington, DC 20010, USA.ORCID 0000-0002-4198-4021
Alan A SagDivision of Vascular and Interventional Radiology, Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA.
Allison LaxDepartment of Radiology, MedStar Georgetown University Hospital, Washington, DC 20007, USA.
Kevin W ParkDepartment of Orthopaedic Surgery, MedStar Georgetown University Hospital, Washington, DC 20007, USA.
Alexander KheradiDepartment of Emergency Medicine, MedStar Georgetown University Hospital, Washington, DC 20007, USA.
Marc SapovalHôpital Européen Georges-Pompidou, 75015 Paris, France.
Jafar GolzarianNorth Star Vascular and Interventional Institute, Minnesota, MN 55427, USA.
Peiman HabibollahiDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Osman AhmedDivision of Interventional Radiology, Rush University Medical Center, Chicago, IL 60612, USA.
Shamar YoungDivision of Interventional Radiology, Department of Medical Imaging, University of Arizona Medical Center, Tucson, AZ 85712, USA.ORCID 0000-0002-7967-7166
Nariman NezamiGeorgetown University School of Medicine, Washington, DC 20007, USA.ORCID 0000-0002-8829-1548

Funding

TLTC innovation grant at the University of Maryland at College Park T2117
6 · The paper itself

Abstract

Knee osteoarthritis (KOA) is a musculoskeletal disorder characterized by articular cartilage degeneration and chronic inflammation, affecting one in five people over 40 years old. The purpose of this study was to provide an overview of traditional and novel minimally invasive treatment options and role of artificial intelligence (AI) to streamline the diagnostic process of KOA. This literature review provides insights into the mechanisms of action, efficacy, complications, technical approaches, and recommendations to intra-articular injections (corticosteroids, hyaluronic acid, and plate rich plasma), genicular artery embolization (GAE), and genicular nerve ablation (GNA). Overall, there is mixed evidence to support the efficacy of the intra-articular injections that were covered in this study with varying degrees of supported recommendations through formal medical societies. While GAE and GNA are more novel therapeutic options, preliminary evidence supports their efficacy as a potential minimally invasive therapy for patients with moderate to severe KOA. Furthermore, there is evidentiary support for the use of AI to assist clinicians in the diagnosis and potential selection of treatment options for patients with KOA. In conclusion, there are many exciting advancements within the diagnostic and treatment space of KOA.

Indexed as

AI for knee osteoarthritisgenicular artery embolizationgenicular nerve ablationknee osteoarthritisregenerative and experimental therapies

Identifiers

PMID39338224
PMCPMC11432885

What OpenQuestion holds

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Registered trials

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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.