Deep-learning based segmentation of ultrasound adipose image for liposuction.

BACKGROUND: To develop an automatic and reliable ultrasonic visual system for robot- or computer-assisted liposuction, we examined the use of deep learning for the segmentation of adipose ultrasound images in clinical and educational settings. METHODS: To segment adipose layers, it is proposed to use an Attention Skip-Convolutions ResU-Net (Attention SCResU-Net) consisting of SC residual blocks, attention gates and U-Net architecture. Transfer learning is utilised to compensate for the deficiency of clinical data. The Bama pig and clinical human adipose ultrasound image datasets are utilized, respectively. RESULTS: The final model obtains a Dice of 99.06 ± 0.95% and an ASD of 0.19 ± 0.18 mm on clinical datasets, outperforming other methods. By fine-tuning the eight deepest layers, accurate and stable segmentation results are obtained. CONCLUSIONS: The new deep-learning method achieves the accurate and automatic segmentation of adipose ultrasound images in real-time, thereby enhancing the safety of liposuction and enabling novice surgeons to better control the cannula.

Facial Rejuvenation and Contouring with Radiofrequency-Assisted Procedures in Asians.

Radiofrequency (RF)-assisted liposuction treatment is a minimally invasive skin-tightening technique for the patient population with skin laxity. The authors recommend facial liposuction combined with the RF procedure for the treatment of skin laxity. Minimal-invasive liposuction creates working channels for RF treatment and sufficiently exposes the subdermal fibrous septal network tissue so that the RF energy can directly act on the collagen of the fibrous septal network for thermal shrinkage, leading to better surgical results. In this article, the authors describe their preferred technique and experience for face rejuvenation and contouring.

An explainable machine learning method for assessing surgical skill in liposuction surgery.

PURPOSE: Surgical skill assessment has received growing interest in surgery training and quality control due to its essential role in competency assessment and trainee feedback. However, the current assessment methods rarely provide corresponding feedback guidance while giving ability evaluation. We aim to validate an explainable surgical skill assessment method that automatically evaluates the trainee performance of liposuction surgery and provides visual postoperative and real-time feedback. METHODS: In this study, machine learning using a model-agnostic interpretable method based on stroke segmentation was introduced to objectively evaluate surgical skills. We evaluated the method on liposuction surgery datasets that consisted of motion and force data for classification tasks. RESULTS: Our classifier achieved optimistic accuracy in clinical and imitation liposuction surgery models, ranging from 89 to 94%. With the help of SHapley Additive exPlanations (SHAP), we deeply explore the potential rules of liposuction operation between surgeons with variant experiences and provide real-time feedback based on the ML model to surgeons with undesirable skills. CONCLUSION: Our results demonstrate the strong abilities of explainable machine learning methods in objective surgical skill assessment. We believe that the machine learning model based on interpretive methods proposed in this article can improve the evaluation and training of liposuction surgery and provide objective assessment and training guidance for other surgeries.

Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform.

Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.

The Effect of Liposuction Cannula Diameter on Fat Retention-Based on a Rheological Simulation.

BACKGROUND: Autologous fat is considered as an ideal material for soft-tissue augmentation in plastic and reconstructive surgery. The primary drawback of autologous fat grafting is the high absorption rate, thus fat retention is considered as an essential indicator. There are several researches about the factors that can influence fat retention, including centrifugation and cannula size. However, rheological models of cannula during liposuction are limited. This research focuses on the effects of cannulas with diameters of 2 mm and 2.5 mm on fat retention, which is based on a rheological simulation of inlet pressure and maximum velocity. Experiments on mice were also conducted to confirm the result from the simulation. METHODS: A simulation was conducted with the physical parameters of the adipose tissue. Human lipoaspirate samples were obtained from patients by liposuction through cannulas of different diameters and were transferred into subcutaneous tissue of nude mice, a part of which were used in viscosity and density measurement. Graft retention was measured and fat quality was assessed through histologic analysis after 6 months. RESULTS: Viscosity and density of the fat tissue had significant effects on fat retention. The 2.5 mm diameter cannula had significantly lower inlet pressure and maximum velocity and thus led to higher graft retention, but oil cystic nodules appeared meanwhile. CONCLUSIONS: Cannulas with larger diameters have lower inlet pressure and maximum velocity during the liposuction process, which further influences the viability of adipocytes and adipose stem cells and thus has larger fat graft retention. This research built a mathematical model with less bias than in vivo experiments and provides a general way for analyzing the outcome of a liposuction precisely, which adds to the data for cannula optimization.