Discoid medial meniscus: a systematic review of clinical presentation, surgical management and outcomes

Abstract

Purpose: To systematically synthesize the literature dedicated to discoid medial meniscus (DMM) and to describe its clinical presentation, imaging and arthroscopic features, morphology, associated pathology, management, outcomes, and temporal reporting patterns. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched through 24 June 2026. Original human reports of radiologically or intraoperatively confirmed classic DMM with extractable clinical, imaging, treatment, or outcome data were eligible. Reports limited to discoid lateral meniscus, nonclinical anatomical studies, reviews without unique cases, duplicate cohorts, or without separable DMM data were excluded. Data were synthesized descriptively with variable-specific denominators; quality was assessed using design-appropriate JBI or MINORS tools. Results: Ninety reports described 179 patients/cases and 241 DMM knees; 117/164 were male (71.3%). Bilateral DMM was reported in 62/149 patients (41.6%), complete morphology in 103/151 knees (68.2%), and tears in 149/196 knees (76.0%). Surgery was reported in 180/241 knees (74.7%): rim-preserving procedures in 142/180 operated knees (78.9%), total meniscectomy in 29 (16.1%), repair in 34 (18.9%), and allograft transplantation in 3 (1.7%). Contemporary reports more often described MRI, rim preservation, and repair than historical reports. Conclusions: Evidence consisted predominantly of case reports and small series. Bilateral involvement, complete morphology, and tears were frequently reported, while contemporary literature more often described MRI, arthroscopy, and meniscal preservation. These are published-literature patterns, not population estimates or comparative evidence of treatment effectiveness. Standardized multicenter or registry-based studies are needed. Level of evidence: Level V, systematic review of Level IV-V evidence. (c) 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.