Fast collision detection based on nose augmentation virtual surgery

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

“…The most frequently used algorithms were supervised machine learning (n = 11, 25%) 29 – 31 , 33 , 34 , 47 , 53 , 54 , 58 , 59 , 65 and artificial neural networks (n = 11, 25%). 36 , 40 , 42 , 43 , 50 , 57 , 60 – 64 Other algorithms used were convolutional neural networks (n = 8, 19%), 25 , 27 , 28 , 37 , 38 , 39 , 49 , 56 unsupervised machine learning (n = 4, 9%), 41 , 45 , 55 , 68 natural language processing (n = 4, 9%), 34 , 48 , 67 , 68 generative adversarial networks (n = 2, 5%), 17 , 26 computer vision (n = 2, 5%), 32 , 52 and combinations of models (combo; n = 2, 5%). 43 , 44 Input features were typically comprised of raw and preprocessed variables, such as subject characteristics (age, lapse time, comorbidities, vital signs, and laboratory values, anatomical and wound measurements, tissue reflectance spectrum), clinical images (facial photography, CT images, angiography, photoplethysmography, dermatoscopy, 3D cephalograms), surgical factors (surgical approach, intraoperative interactions with equipment), and synthetic or experimentally derived metrics (external muscle stimulation pulse widths, frequently asked questions).…”

“…All but eight studies (n = 36, 82%) provided disclosures. A study aim was clearly stated in all papers, and all but two articles 67 , 68 reported the data source used (n = 42, 95%). Most papers reported equivalent comparison groups (n = 35, 76%) though fewer studies compared AI to an adequate control group (ie, a gold standard diagnostic test or therapeutic intervention) (n = 28, 64%) or a contemporary, nonhistorical, ground truth (n = 29, 66%).…”

“…AI was frequently used to predict indications for surgical reconstruction, 17,24,25,30,31,33,43,57,62,64 the likelihood of clinical and technical outcomes (blood loss, 65 swelling‚ 50 response to antibiotics, 40 wound healing, 35,57,66 surgical site infection‚ 62 flap failure‚ 55 and overall survival 64 ), anatomical landmarks for surgical planning (skeletal profiles, 30,31,37 midfacial plane, 29 and perforators 52 ), and measurements of qualitative postoperative success (improvements in aesthetics, 28,29,49,53,56,60 form, 61,63 and function 32,47,54,67 ). The most popular subspecialty was aesthetic and breast surgery (n = 12, 27%), 25–28,47,49,52,55,56,60,61,68 followed by craniofacial surgery (n = 10, 23%), 29–32,37,41,51,54,67,68 nonbreast microsurgery (n = 6, 14%), 39,42,44,45,47,62 burn surgery (n = 5, 11%), 33,40,57,58,64 general plastic surgery (n = 4, 9%), 17,34,48,69 oral and maxillofacial surgery (n = 3, 7%), 43,50,65 wound care (n = 2, 5%), 35,59 and hand surgery (n = 2, 5%). 36,46 AI models were most often in study phases 0 (n = 19) 29–31,33,37–39,41–46,57,…”

“…All but eight studies (n = 36, 82%) provided disclosures. A study aim was clearly stated in all papers, and all but two articles 67,68 reported the data Fig. 3.…”

Hybrid Hierarchical Collision Detection Based on Data Reuse

“…Xie [19] combines hierarchical bounding sphere with GPU acceleration to simulate collision detection among rigid bodies for rhinoplasty. Shen [20] adopts the mixed bounding volume hierarchy tree to detect the potential collision object sets quickly, and then uses the streaming pattern algorithm for accurate collision detection.…”