Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation

Thighs lift in the post-bariatric patient - A systematic review

BACKGROUND

Thigh lift, first described by Lewis in 1957, consists of thigh recontouring by various strategies. In post-bariatric thigh lift (PBTL), the technical details become fundamental due to both patient comorbidities and increased risk of complications. Moreover, post-bariatric weight loss affects the thighs, resulting in significant tissue redundancy, inner excess, lower thigh deformity, later excess, and buttocks ptosis. With the present paper, a systematic review of PBTL procedures is reported and a comprehensive classification system is proposed, aiming to improve their medical and surgical management.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review was carried out by searching the PubMed (MEDLINE) database from May 2004 to May 2024 using the search string "thighplasty OR thigh lift OR post-bariatric thighplasty OR (thigh lift AND weight loss) OR (thigh lift AND liposuction)". Original studies discussing PBTL with a minimum of three clinical cases were eligible for inclusion.

RESULTS

The final synthesis included 17 articles and 496 patients. The articles were published in the last 20 years. Several papers discussed significant PBTL surgical strategies and technical measures.

CONCLUSIONS

PBTL is challenging because of both technical factors and complex comorbidities of post-bariatric patients. This comprehensive assessment of PBTL may help in choosing the appropriate treatment based on a patient's individual needs. Liposuction-assisted inner thigh lift with combined horizontal-vertical scars and skin-only excision is effective and versatile for most patients. However, select cases may benefit from alternative and more invasive strategies. Artificial intelligence is a topic of growing interest, and it will probably become increasingly relevant in PBTL.

Sexual dimorphism in peri-articular tissue anatomy - More keys to understanding sex-differences in osteoarthritis?

Objective

Osteoarthritis prevalence differs between women and men; whether this is the result of differences in pre-morbid articular or peri-articular anatomical morphotypes remains enigmatic. Albeit sex within humans cannot be reduced to female/male, this review focusses to the sexual dimorphism of peri-articular tissues, given lack of literature on non-binary subjects.

Methods

Based on a Pubmed search and input from experts, we selected relevant articles based on the authors' judgement of relevance, interest, and quality; no "hard" bibliometric measures were used to evaluate the quality or importance of the work. Emphasis was on clinical studies, with most (imaging) data being available for the knee and thigh.

Results

The literature on sexual dimorphism of peri-articular tissues is reviewed: 1) bone size/shape, 2) subchondral/subarticular bone, 3) synovial membrane and infra-patellar fad-pad (IPFP), 4) muscle/adipose tissue, and 5) peri-articular tissue response to treatment.

Conclusions

Relevant sex-specific differences exist for 3D bone shape and IPFP size, even after normalization to body weight. Presence of effusion- and Hoffa-synovitis is associated with greater risk of incident knee osteoarthritis in overweight women, but not in men. When normalized to bone size, men exhibit greater muscle, and women greater adipose tissue measures relative to the opposite sex. Reduced thigh muscle specific strength is associated with incident knee osteoarthritis and knee replacement in women, but not in men. These observations may explain why women with muscle strength deficits have a poorer prognosis than men with similar deficits. A "one size/sex fits all" approach must be urgently abandoned in osteoarthritis research.

Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques

BACKGROUND

Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for clinicians. This non-interventional retrospective study aims to develop two deep learning (DL) systems to efficiently, accurately, and instantly detect the head position on LCRs.

METHODS

LCRs from 13 centers were reviewed and a total of 3000 radiographs were collected and divided into 2400 cases (80.0 %) in the training set and 600 cases (20.0 %) in the validation set. Another 300 cases were selected independently as the test set. All the images were evaluated and landmarked by two board-certified orthodontists as references. The head position of the LCR was classified by the angle between the Frankfort Horizontal (FH) plane and the true horizontal (HOR) plane, and a value within - 3°- 3° was considered normal. The YOLOv3 model based on the traditional fixed-point method and the modified ResNet50 model featuring a non-linear mapping residual network were constructed and evaluated. Heatmap was generated to visualize the performances.

RESULTS

The modified ResNet50 model showed a superior classification accuracy of 96.0 %, higher than 93.5 % of the YOLOv3 model. The sensitivity&recall and specificity of the modified ResNet50 model were 0.959, 0.969, and those of the YOLOv3 model were 0.846, 0.916. The area under the curve (AUC) values of the modified ResNet50 and the YOLOv3 model were 0.985 ± 0.04 and 0.942 ± 0.042, respectively. Saliency maps demonstrated that the modified ResNet50 model considered the alignment of cervical vertebras, not just the periorbital and perinasal areas, as the YOLOv3 model did.

CONCLUSIONS

The modified ResNet50 model outperformed the YOLOv3 model in classifying head position on LCRs and showed promising potential in facilitating making accurate diagnoses and optimal treatment plans.

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

BACKGROUND

Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized.

PURPOSE

Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population.

STUDY TYPE

Retrospective based on prospectively acquired data.

POPULATION

Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females).

FIELD STRENGTH/SEQUENCE

A 3-T, quantitative double-echo steady state (qDESS).

ASSESSMENT

Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage.

STATISTICAL TESTS

Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant.

RESULTS

DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec.

DATA CONCLUSION

The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Intermuscular adipose tissue in Type 2 diabetes mellitus: Non-invasive quantitative imaging and clinical implications

Intermuscular adipose tissue (IMAT) is an ectopic fat depot found beneath the fascia and within the muscles. IMAT modulates muscle insulin sensitivity and triggers local and systemic chronic low-grade inflammation by producing cytokines and chemokines, which underlie the pathogenesis of Type 2 diabetes mellitus (T2DM). Imaging techniques have been increasingly used to non-invasively quantify IMAT in patients with diabetes in research and healthcare settings. In this study, we systematically reviewed the cell of origin and definition of IMAT, and the use of quantitative and functional imaging technology pertinent to the etiology, risk factors, lifestyle modification, and therapeutic treatment of diabetes. The purpose of this article is to provide important insight into the current understanding of IMAT and future prospects of targeting IMAT for T2DM control.