Dataset Information

Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.

ABSTRACT: Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = - 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.

SUBMITTER: Ro K

PROVIDER: S-EPMC8302634 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundArtificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice.AimTo train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.MethodsWith high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.ResultsThe CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time (P = 0.32; P = 0.16, respectively).ConclusionCADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.

Project description:This study investigated body composition measures in highly trained and untrained individuals using whole-body magnetic resonance imaging (MRI). Additionally, correlations between these measures and skeletal muscle gene expression were performed. Thirty-six individuals were included: endurance-trained males (ME, n = 8) and females (FE, n = 7), strength-trained males (MS, n = 7), and untrained control males (MC, n = 8) and females (FC, n = 6). MRI scans were performed, and resting M. vastus lateralis (VL) biopsies were subjected to RNA sequencing. Liver fat fraction, visceral adipose tissue volume (VAT), total body fat, and total lean tissue were measured from MRI data. Additionally, cross-sectional area (CSA) and fat signal fraction (FSF) were calculated from Mm. pectoralis, M. erector spinae and M. multifidus combined, Mm. quadriceps, and Mm. triceps surae (TS). Liver fat fraction, VAT, and total body fat relative to body weight were lower in ME and FE compared with corresponding controls. MS had a larger CSA across all four muscle groups and lower FSF in all muscles apart from TS compared with MC. ME had a lower FSF across all muscle groups and a larger CSA in all muscles except TS than MC. FE athletes showed a higher CSA in Mm. pectoralis and Mm. quadriceps and a lower CSA in TS than FC with no CSA differences found in the back muscles investigated. Surprisingly, the only difference in FSF between FE and FC was found in Mm. pectoralis. Lastly, correlations between VL gene expression and VL CSA as well as FSF showed that genes positively correlated with CSA revealed an enrichment of the oxidative phosphorylation and thermogenesis pathways, while the genes positively correlated with FSF showed significant enrichment of the spliceosome pathway. Although limited differences were found with training in females, our study suggests that both regular endurance and resistance training are useful in maintaining muscle mass, reducing adipose tissue deposits, and reducing muscle fat content in males.

Project description:BackgroundThe effect of fat infiltration in the paraspinal muscles on cervical degenerative disease has been confirmed by multiple studies. However, little is known about fat infiltration in the paraspinal extensors in patients with acute cervical spinal cord injury (SCI). This study aimed to investigate the difference in paraspinal extensor fatty infiltration between patients with acute cervical SCI and healthy controls, and to further explore the protective role of the paravertebral extensor muscles in patients with cervical SCI.MethodsA total of 50 patients with acute cervical SCI admitted to the emergency department from January 2019 to November 2023 were retrospectively analyzed, including 26 males and 24 females, with an average age of 59.60 ± 10.81 years. A control group of 50 healthy middle-aged and elderly individuals was also included, comprising 28 males and 22 females, with an average age of 55.00 ± 8.21 years. Cervical spine magnetic resonance imaging (MRI) was used to measure the cross-sectional areas of the superficial and deep cervical extensor muscles, the corresponding vertebral body cross-sectional areas, and the fat area within the superficial and deep extensor muscle groups using Image J software. Differences between the two groups were compared, and the cervical SCI patients were further analyzed based on the severity of the spinal cord injury and gender differences.ResultsThe deep fatty infiltration ratio (DFIR) and superficial fatty infiltration ratio (SFIR) at C4-C7 in the cervical SCI group were significantly higher than those in the control group (P < 0.001). The cross-sectional area of the functional deep extensor area (FDEA) relative to the vertebral body area (VBA) and the cross-sectional area of the functional superficial extensor area (FSEA) relative to the VBA at the C5 and C6 levels in the cervical SCI group were significantly lower than those in the control group (P < 0.001, P < 0.001, P = 0.034, P = 0.004 respectively). Among the cervical SCI patients, the cross-sectional areas of the deep extensor area (DEA) and the superficial extensor area (SEA) in males were significantly higher than those in females (P < 0.001). At the C6 and C7 levels, the FDEA/VBA and FSEA/VBA ratios in the male group were higher than those in the female group (P = 0.009, P = 0.022, P = 0.019, P = 0.005, respectively).ConclusionPatients with acute cervical SCI exhibit significantly higher fatty infiltration and a greater degree of paravertebral extensor muscle degeneration compared to healthy controls. This finding underscores the importance of the paravertebral extensor muscles in the context of cervical SCI and may guide future therapeutic strategies.

Project description:IntroductionBoth Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases.MethodsWe performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning.Results(1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion.ConclusionsAutomatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

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Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI.

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