Degenerative medial and lateral menisci root tears: demographics, clinical presentation, imaging features, and associated findings.

PURPOSE: (I) Characterize the demographics and clinical features of patients with meniscal root tears (MRT); (II) analyze the morphology, extent, and grade of MRT on MRI; (III) evaluate associated abnormalities on imaging; and (IV) evaluate the associations between imaging findings, demographics, clinical features, and joint structural abnormalities. MATERIAL AND METHODS: A search was performed to identify meniscal root tears. Age, sex, BMI, and pain were recorded. Knee radiographs and MRI were reviewed. Presence, grade and morphology of MRT, meniscal extrusion, insufficiency fractures, as well as joint structural abnormalities were scored. For goals (I), (II), and (III), tabulations for categorical variables and mean for continuous variables were computed. MRT findings variables were described using percentages. For goal (IV), adjusted linear and logistic regression were employed. RESULTS: Ninety-six patients with a mean age of 56.6 years (69 females) and mean BMI of 28.9 kg/m2 were included; 88 of the MRT were located at the posterior horn of the medial meniscus (PHMM), and 82% were radial tear. The mean tear diameter was 3.8 mm, and 78/96 tears presented with meniscal extrusion. Nineteen patients presented with subchondral insufficiency fracture (SIF), which was significantly associated with the gap of the tear (p = 0.001) and grade of the meniscal root lesion (p = 0.005). CONCLUSION: MRT typically found in middle-aged to older overweight and obese women. Lesions were mostly radial tears and located at PHMM and were frequently associated with meniscal extrusion and SIF. Moreover, the presence of SIF was significantly associated with the gap width and grade of root tear.

Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset.

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy (HIE) using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High Dose Erythropoietin for Asphyxia (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25th, 2017 and October ninth, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment [NDI] at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on a test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 100% of cases from 2 institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4, 232 males, 182 females), in the study cohort, 198 (48%) died or had any NDI at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60-0.86) and 63% accuracy on the in-distribution test set and an AUC of 0.77 (95% CI: 0.63-0.90) and 78% accuracy on the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. ©RSNA, 2024.

Comparing cost and print time estimates for six commercially-available 3D printers obtained through slicing software for clinically relevant anatomical models.

BACKGROUND: 3D printed patient-specific anatomical models have been applied clinically to orthopaedic care for surgical planning and patient education. The estimated cost and print time per model for 3D printers have not yet been compared with clinically representative models across multiple printing technologies. This study investigates six commercially-available 3D printers: Prusa i3 MK3S, Formlabs Form 2, Formlabs Form 3, LulzBot TAZ 6, Stratasys F370, and Stratasys J750 Digital Anatomy. METHODS: Seven representative orthopaedic standard tessellation models derived from CT scans were imported into the respective slicing software for each 3D printer. For each printer and corresponding print setting, the slicing software provides a print time and material use estimate. Material quantity was used to calculate estimated model cost. Print settings investigated were infill percentage, layer height, and model orientation on the print bed. The slicing software investigated are Cura LulzBot Edition 3.6.20, GrabCAD Print 1.43, PreForm 3.4.6, and PrusaSlicer 2.2.0. RESULTS: The effect of changing infill between 15% and 20% on estimated print time and material use was negligible. Orientation of the model has considerable impact on time and cost with worst-case differences being as much as 39.30% added print time and 34.56% added costs. Averaged across all investigated settings, horizontal model orientation on the print bed minimizes estimated print time for all 3D printers, while vertical model orientation minimizes cost with the exception of Stratasys J750 Digital Anatomy, in which horizontal orientation also minimized cost. Decreasing layer height for all investigated printers increased estimated print time and decreased estimated cost with the exception of Stratasys F370, in which cost increased. The difference in material cost was two orders of magnitude between the least and most-expensive printers. The difference in build rate (cm3/min) was one order of magnitude between the fastest and slowest printers. CONCLUSIONS: All investigated 3D printers in this study have the potential for clinical utility. Print time and print cost are dependent on orientation of anatomy and the printers and settings selected. Cost-effective clinical 3D printing of anatomic models should consider an appropriate printer for the complexity of the anatomy and the experience of the printer technicians.

Identifying a commercially-available 3D printing process that minimizes model distortion after annealing and autoclaving and the effect of steam sterilization on mechanical strength.

BACKGROUND: Fused deposition modeling 3D printing is used in medicine for diverse purposes such as creating patient-specific anatomical models and surgical instruments. For use in the sterile surgical field, it is necessary to understand the mechanical behavior of these prints across 3D printing materials and after autoclaving. It has been previously understood that steam sterilization weakens polylactic acid, however, annealing heat treatment of polylactic acid increases its crystallinity and mechanical strength. We aim to identify an optimal and commercially available 3D printing process that minimizes distortion after annealing and autoclaving and to quantify mechanical strength after these interventions. METHODS: Thirty millimeters cubes with four different infill geometries were 3D printed and subjected to hot water-bath annealing then immediate autoclaving. Seven commercially available 3D printing materials were tested to understand their mechanical behavior after intervention. The dimensions in the X, Y, and Z axes were measured before and after annealing, and again after subsequent autoclaving. Standard and strength-optimized Army-Navy retractor designs were printed using the 3D printing material and infill geometry that deformed the least. These retractors were subjected to annealing and autoclaving interventions and tested for differences in mechanical strength. RESULTS: For both the annealing and subsequent autoclaving intervention, the material and infill geometry that deformed the least, respectively, was Essentium PLA Gray and "grid". Standard retractors without intervention failed at 95 N +/- 2.4 N. Annealed retractors failed at 127.3 N +/- 10 N. Autoclave only retractors failed at 15.7 N +/- 1.4 N. Annealed then autoclaved retractors failed at 19.8 N +/- 3.1 N. Strength-optimized retractors, after the annealing then autoclaving intervention, failed at 164.8 N +/- 12.5 N. CONCLUSION: For 30 mm cubes, the 3D printing material and infill geometry that deformed the least, respectively, was Essentium PLA and "grid". Hot water-bath annealing results in increased 3D printed model strength, however autoclaving 3D prints markedly diminishes strength. Strength-optimized 3D printed PLA Army-Navy retractors overcome the strength limitation due to autoclaving.

3D printed PLA Army-Navy retractors when used as linear retractors yield clinically acceptable tolerances.

BACKGROUND: Modern low-cost 3D printing technologies offer the promise of access to surgical tools in resource scarce areas, however optimal designs for manufacturing have not yet been established. We explore how the optimization of 3D printing parameters when manufacturing polylactic acid filament based Army-Navy retractors vastly increases the strength of retractors, and investigate sources of variability in retractor strength, material cost, printing time, and parameter limitations. METHODS: Standard retractors were printed from various polylactic acid filament spools intra-manufacturer and inter-manufacturer to measure variability in retractor strength. Printing parameters were systematically varied to determine optimum printing parameters. These parameters include retractor width, thickness, infill percentage, infill geometry, perimeter number, and a reinforced joint design. Estimated retractor mass from computer models allows us to estimate material cost. RESULTS: We found statistically significant differences in retractor strength between spools of the same manufacturer and between manufacturers. We determined the true strength optimized retractor to have 30% infill, 3 perimeters, 0.25 in. thickness, 0.75 in. width, and has "Triangle" infill geometry and reinforced joints, failing at more than 15X the threshold for clinically excessive retraction and costs $1.25 USD. CONCLUSIONS: The optimization of 3D printed Army-Navy retractors greatly improve the efficacy of this instrument and expedite the adoption of 3D printing technology in many diverse fields in medicine not necessarily limited to resource poor settings.