Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_537_Библиотеки_им_академика_М_И_Перельмана
.pdf
& Piffko, J. (2000). Experimental and finite-element models for the assessment of mandibular
deformation under mechanical loading. Mund-, Kiefer- und Gesichtschirurgie, 4, 14–20.
48. Vollmer, D., Meyer, U., Joos, U., Vegh, A., & Piffko, J. (2000). Experimental and finite
element study of a human mandible. Journal of Cranio-Maxillo-Facial Surgery, 28, 91–96.
49. Korioth, T. W., Romilly, D. P., & Hannam, A. G. (1992). Three-dimensional finite element
stress analysis of the human dentate mandible. American Journal of Physical Anthropology, 88,
69–96.
50. Hylander, W. L. (1984). Stress and strain in the mandibular symphysis of primates: A test of
competing hypotheses. American Journal of Physical Anthropology, 64, 1–46.
51. Dey, J. K., Recker, C. A., Olson, M. D., Bowen, A. J., Panda, A., Kostandy, P. M., et al. (2019).
Assessing nasal soft-tissue envelope thickness for rhinoplasty: Normative data and a predictive
algorithm. JAMA Facial Plastic Surgery, 21(6), 511–517.
52. Saban, Y., Andretto Amodeo, C., Hammou, J. C., & Polselli, R. (2008). An anatomical study of
the nasal superficial musculoaponeurotic system: Surgical applications in rhinoplasty. Archives
of Facial Plastic Surgery, 10, 109–115.
53. Griesman, B. L. (1944). Muscles and cartilages of the nose from the standpoint of a typical
rhinoplasty. Archives of Otolaryngology, 39, 334–341.
54. Gunter, J. P. (1993). Secondary rhinoplasty: The open approach. In R. K. Daniel (Ed.),
Aesthetic plastic surgery: Rhinoplasty. Little Brown.
55. Toriumi, D. M. (1995). Caudal septal extension graft for correction of the retracted columella.
Oper Tech Otolaryngol – Head Neck Surgery, 6, 311–318.
56. Calvert, J. W., Patel, A. C., & Daniel, R. K. (2014). Reconstructive rhinoplasty: Operative
revision of patients with previous autologous costal cartilage grafts. Plastic and Reconstructive
Surgery, 133, 1087–1096.
57. Daniel, R. K., & Pálházi, P. (2018). Rhinoplasty: An anatomical and clinical atlas. Springer.
58. Daniel, R. K., & Letourneau, A. (1988). Rhinoplasty: nasal anatomy. Annals of Plastic Surgery,
20, 5–13.
59. Pappachan, B., & Alexander, M. (2012). Biomechanics of cranio-maxillofacial trauma. Journal
of Maxillofacial and Oral Surgery, 11(2), 224–230.
60. Rowe, N. L. (1994). Aetiology of injury chap. 2. In N. L. Rowe & J. L. I. William (Eds.),
Maxillofacial injuries (Vol. 1, 2nd ed., pp. 39–50). Churchill Living Stone.
61. Reilly, P. L., & Simpson, D. A. (1995). Craniocerebral injuries, chap. 13. In D. J. David & D.
A. Simpson (Eds.), Craniomaxillofacial trauma (pp. 367–396). Churchill Livingstone.
62. Luce, E. A., Tubb, T. D., & Moore, A. M. (1979). Review of 1000 major facial fractures and
associated injury. Plastic and Reconstructive Surgery, 63(1), 26.
63.
Chang, C. J., Chen, Y. R., Noordhoff, M. S., et al. (1994). Maxillary involvement in central
https://t.me/medicina_free
craniofacial fractures with associated head injuries. The Journal of Trauma, 37(5), 807.
64. Janovic, A., Saveljic, I., Vukicevic, A., & Djuric, M. (2015). Occlusal load distribution through
the cortical and trabecular bone of the human mid-facial skeleton in natural dentition: A threedimensional fnite element study. Annals of Anatomy, 197, 16–23.
65. Alexandridis, C., Caputo, A. A., & Thanos, C. E. (1985). Distribution of stresses in the human
skull. Journal of Oral Rehabilitation, 12(6), 499–507.
66. Tokgöz, E., Levitt, S., Patel, V., Carola, N., & Sosa, D. (2023). Biomechanics of total knee
arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and science
concepts. Springer. ISBN #: 978-3-031-31099-7.
67. Luo, X., Huang, H., Yin, X., Shi, B., & Li, J. (2019). Functional stability analyses of
maxillofacial skeleton bearing cleft deformities. Scientific Reports, 9(1), 4261–4270.
68. Harikrishnan, P., & Balakumaran, V. (2017). Analysis of stress trajectories in human adult cleft
skull. The Journal of Craniofacial Surgery, 28(6), 1552–1553.
69. Zhao, L., Patel, P. K., & Harris, G. F. (2004). Stress analysis of unilateral cleft palate using a
three dimensional fnite element model of pediatric subject-specifc maxilla. Conference
Proceedings: Annual International Conference of the IEEE Engineering in Medicine and
Biology Society, 2004, 5077–5079.
70. Tian, T., Huang, H. Y., Wang, W., et al. (2022). Three-dimensional finite element analysis of
the effect of alveolar cleft bone graft on the maxillofacial biomechanical stabilities of unilateral
complete cleft lip and palate. Biomedical Engineering Online, 21, 31. https:// doi. org/ 10. 1186/
s12938-022-01000-y
[Crossref]
71. Einafshar, M. (2022). Biomechanical stability of two different maxillofacial screws in a rabbit
model. Journal of Regeneration, Reconstruction & Restoration (Triple R).
72. de Ulloa, J. L., González, J. E., Beltrán, A. M., Avés, E. P., Rodríguez-Guerra, J., & Torres, Y.
(2022). Biomechanical behavior of customized scaffolds: A three-dimensional finite element
analysis. Materials & Design, 223, 111173.
73. Erdem, N. F., Abdioğlu, G., Ekinci, S. A., & Şişman, A. Ç. (2022). Radiological evaluation and
biomechanical stability of implants simultaneously placed at the sites with sole advanced
platelet-rich fibrin (A-PRF) grafted sinus floor. Journal of Maxillofacial and Oral Surgery, 8,
1–8.
74. Ma, H., Teng, H., Li, A., Zhang, Z., Zheng, T., Chong, D. Y., et al. (2022). The pressure in the
temporomandibular joint in the patients with maxillofacial deformities. Journal of Stomatology,
Oral and Maxillofacial Surgery.
75. Feng, Y., Teng, H., Shu, J., Shao, B., Chong, D. Y., & Liu, Z. (2022). Biomechanical
comparison of the effect of bilateral sagittal split ramus osteotomy with or without Le Fort I
osteotomy on the temporomandibular joints of the patients with maxillofacial deformities under
centric occlusion. Computer Methods in Biomechanics and Biomedical Engineering, 10, 1–10.
76.
Tribst, J. P. M., Dal Piva, A. M. D. O., & Kalman, L. (2022). Stress concentration of hybrid
https://t.me/medicina_free
occlusal splint-mouthguard during a simulated maxillofacial traumatic impact: 3D-FEA.
Dentistry Journal, 10(4), 65.
77. Zhong, S., Shi, Q., Van Dessel, J., Gu, Y., Sun, Y., Yang, S., & Politis, C. (2022).
Biomechanical validation of structural optimized patient-specific mandibular reconstruction
plate orienting additive manufacturing. Computer Methods and Programs in Biomedicine, 224,
107023.
78. Armenàkas, A. E. (2016). Advanced mechanics of materials and applied elasticity. CRC Press.
[zbMATH]
79. Zeller, A. N., Neuhaus, M. T., Weissbach, L. V. M., Rana, M., Dhawan, A., Eckstein, F. M.,
Gellrich, N. C., & Zimmerer, R. M. (2020). Patient-specific mandibular reconstruc-tion plates
increase accuracy and long-term stability in immediate alloplas-tic reconstruction of segmental
mandibular defects. Journal of Oral and Maxillofacial Surgery, 19, 609–615.
80. Wilde, F., Hanken, H., Probst, F., Schramm, A., Heiland, M., & Cornelius, C. P. (2015). Mul-
ticenter study on the use of patient-specific CAD/CAM reconstruction plates for mandibular
reconstruction. International Journal of Computer Assisted Radiology and Surgery, 10, 2035–
2051.
81. Narra, N., Valasek, J., Hannula, M., Marcian, P., Sandor, G. K., Hyttinen, J., & Wolff, J. (2014).
Finite element analysis of customized reconstruction plates for mandibular continuity defect
therapy. Journal of Biomechanics, 47, 264–268.
82. Al-Ahmari, A., Nasr, E. A., Moiduddin, K., Anwar, S., Kindi, M. A., & Kamrani, A. (2015). A
comparative study on the customized design of mandibular reconstruction plates using finite
element method. Advances in Mechanical Engineering, 7, 2015.
83. Pinheiro, M., & Alves, J. L. (2015). The feasibility of a custom-made endoprosthesis in
mandibular reconstruction: Implant design and finite element analysis. Journal of Cranio-
Maxillofacial Surgery, 43, 2116–2128.
84. Gutwald, R., Jaeger, R., & Lambers, F. M. (2017). Customized mandibular reconstruction
plates improve mechanical performance in a mandibular reconstruction model. Computer
Methods in Biomechanics and Biomedical Engineering, 20, 426–435.
85. Wu, C. H., Lin, Y. S., Liu, Y. S., & Lin, C. L. (2017). Biomechanical evaluation of a novel
hybrid reconstruction plate for mandible segmental defects: A finite element analysis and
fatigue testing. Journal of Cranio-Maxillo-Facial Surgery, 45, 1671–1680.
86. Martola, M., Lindqvist, C., Hanninen, H., & Al-Sukhun, J. (2007). Fracture of titanium plates
used for mandibular reconstruction following ablative tumor surgery. Journal of Biomedical
Materials Research. Part B, Applied Biomaterials, 80, 345–352.
87. Wong, R. C., Tideman, H., Kin, L., & Merkx, M. A. (2010). Biomechanics of mandibular
recon-struction: A review. International Journal of Oral and Maxillofacial Surgery, 39, 313–
319.
88. Memon, A. R., Wang, E., Hu, J., Egger, J., & Chen, X. (2020). A review on computer-aided
design and manufacturing of patient-specific maxillofacial implants. Expert Re-view of Medical
Devices, 17, 345–356.
https://t.me/medicina_free
89.
Huang, M. F., Alfi, D., Alfi, J., & Huang, A. T. (2019). The use of patient-specific implants in
oral and maxillofacial surgery. Oral and Maxillofacial Surgery Clinics of North America, 31,
593–600.
90. Yu, J. C., Buchman, S. R., Gosain, A. K., et al. (2021). Basic biomechanics for craniofacial
surgeons: The responses of alloplastic materials and living tissues to mechanical forces. FACE,
2(4), 446–461. https:// doi. org/ 10. 1177/ 2732501621106023 2
[Crossref]
91. Bartlow, C. M., Mann, K. A., Damron, T. A., & Oest, M. E. (2021). Altered mechanical
behavior of demineralized bone following therapeutic radiation. Journal of Orthopaedic
Research, 39(4), 750–760.
92. Deshpande, S. S., Gallagher, K. K., Donneys, A., et al. (2015). Stem cells rejuvenate radiation-
impaired vasculogenesis in murine distraction osteogenesis. Plastic and Reconstructive
Surgery, 135(3), 799–806.
93. Urlaub, K. M., Lynn, J. V., Carey, E. G., et al. (2018). Histologic improvements in irradiated
bone through pharmaceutical intervention in mandibular distraction osteogenesis. Journal of
Oral and Maxillofacial Surgery, 76(12), 2660–2668.
94. Brandt, M. T., & Haug, R. H. (2003). Open versus closed reduction of adult mandibular
condyle fractures: A review of the literature regarding the evolution of current thoughts on
management. Journal of Oral and Maxillofacial Surgery, 61(11), 1324–1332.
95. Perez, D., & Ellis, E., 3rd. (2020). Complications of mandibular fracture repair and secondary
reconstruction. Seminars in Plastic Surgery, 34(4), 225–231.
96. Lee, Y., Choi, H. G., Shin, D. H., et al. (2014). Subbrow approach as a minimally invasive
reduction technique in the management of frontal sinus fractures. Archives of Plastic Surgery,
41(6), 679–685.
97. Elsayed, S. A., Elsayed, E. H., & Altaweel, A. A. (2021). Stabilization of anterior mandibular
fracture using different osteosynthesis devices: Perioperative clinical notes. Oral and
Maxillofacial Surgery, 25(3), 303–311.
98. Saad, A., Winters, R., Wise, M. W., Dupin, C. L., & St Hilaire, H. (2013). Virtual surgical
planning in complex composite maxillofacial reconstruction. Plastic and Reconstructive
Surgery, 132(3), 626–633.
99. Tokgöz, E., Carola, N., Levitt, S., Patel, V., & Sosa, D. (2023). Robotics applications in total
knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and science
concepts. Springer. ISBN #: 978-3-031-31099-7.
100. Tokgöz, E., Sosa, D., Carola, N., Levitt, S., & Patel, V. (2023). Impact of manufacturing on
total knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3-031-31099-7.
101. Tokgöz, E., Patel, V., Carola, N., Sosa, D., & Levitt, S. (2023). Optimization investigations on
total knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3-031-31099-7.
https://t.me/medicina_free
102.
Tokgöz, E., Patel, V., Sosa, D., Levitt, S., & Carola, N. (2023). Artificial intelligence, deep
learning, and machine learning applications in total knee arthroplasty. In Total knee
arthroplasty: Medical and biomedical engineering and science concepts. Springer. ISBN #:
978-3-031-31099-7.
103. Tokgöz, E. (2023). Advancing engineering of total knee arthroplasty. In Total knee
arthroplasty: Medical and biomedical engineering and science concepts. Springer. ISBN #:
978-3-031-31099-7.
104. Tokgöz, E., & Marina, A. C. (2023). Applications of artificial intelligence, machine learning,
and deep learning on facial plastic surgeries. In Cosmetic and reconstructive facial plastic
surgery: A review of medical and biomedical engineering and science concepts. Springer. ISBN
#: 978-3031311673.
105. Tokgöz, E., & Marina, A. C. (2023). Robotics applications in facial plastic surgeries. In
Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical
engineering and science concepts. Springer. ISBN #: 978-3031311673.
106. Tokgöz, E., & Marina, A. C. (2023). Engineering psychology of facial plastic surgery patients.
In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical
engineering and science concepts. Springer. ISBN #: 978-3031311673.
107. Tokgöz, E. (2023). Technological improvements on facial plastic, head and neck procedures. In
Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical
engineering and science concepts. Springer. ISBN #: 978-3031311673.
108. Levitt, S., Patel, V., Sosa, D., Carola, N., & Tokgöz, E. (2023). Preexisting conditions leading
to total knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3-031-31099-7.
109. Sosa, D., Carola, N., Patel, V., Levitt, S., & Tokgöz, E. (2023). Surgical approach comparison
in total knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3-031-31099-7.
110. Sosa, D., Carola, N., Patel, V., Levitt, S., & Tokgöz, E. (2023). Perioperative patient care for
total knee arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3-031-31099-7.
111. Levitt, S., Patel, V., Carola, N., Sosa, D., & Tokgöz, E. (2023). Complications of total knee
arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and science
concepts. Springer. ISBN #: 978-3-031-31099-7.
112. Carola, N., Patel, V., Levitt, S., Sosa, D., & Tokgöz, E. (2023). Ergonomics of total knee
arthroplasty. In Total knee arthroplasty: Medical and biomedical engineering and science
concepts. Springer. ISBN #: 978-3-031-31099-7.
113. Marina, A. C., & Tokgöz, E. (2023). Non-surgical facial aesthetic procedures. In Cosmetic and
reconstructive facial plastic surgery: A review of medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3031311673.
114.
Marina, A. C., & Tokgöz, E. (2023). Aesthetic surgery of the upper face and cheeks. In
https://t.me/medicina_free
Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical
engineering and science concepts. Springer. ISBN #: 978-3031311673.
115. Marina, A. C., & Tokgöz, E. (2023). Aesthetic surgery of the nose and lower face. In Cosmetic
and reconstructive facial plastic surgery: A review of medical and biomedical engineering and
science concepts. Springer. ISBN #: 978-3031311673.
116. Marina, A. C., Donofrio, G., & Tokgöz, E. (2023). Surgical reconstruction of craniofacial
malformations. In Cosmetic and reconstructive facial plastic surgery: A review of medical and
biomedical engineering and science concepts. Springer. ISBN #: 978-3031311673.
117. Marina, A. C., & Tokgöz, E. (2023). Surgical reconstruction of craniofacial trauma and burns.
In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical
engineering and science concepts. Springer. ISBN #: 978-3031311673.
118. Marina, A. C., & Tokgöz, E. (2023). Cosmetic and reconstructive facial plastic surgery related
simulation and optimization efforts. In Cosmetic and reconstructive facial plastic surgery: A
review of medical and biomedical engineering and science concepts. Springer. ISBN #: 978-
3031311673.
119. Musafer, H., & Tokgöz, E. (2023). A facial wrinkle detection by using deep learning with an
efficient optimizer. In Cosmetic and reconstructive facial plastic surgery: A review of medical
and biomedical engineering and science concepts. Springer. ISBN #: 978-3031311673.
https://t.me/medicina_free
(1)
(2)
©The Author(s), under exclusive license to Springer Nature Switzerland AG2023
E. Tokgöz, M. A. Carro, Cosmetic and Reconstructive Facial Plastic Surgery
https://doi.org/10.1007/978-3-031-31168-0_9
Applications of Artificial Intelligence,
Machine Learning, and Deep Learning on
Facial Plastic Surgeries
EmreTokgöz1 and MarinaA.Carro
2
Whiting School of Engineering, Johns Hopkins University, Baltimore,
MD, USA
The Frank H. Netter M.D. School of Medicine, Quinnipiac University,
North Haven, CT, USA
MarinaA.Carro
Email: Marina.Carro@quinnipiac.edu
Keywords Artificial Intelligence applications on facial plastic surgeries –
Deep learning applications on facial plastic surgeries – Machine learning
applications on facial plastic surgeries
Emre Tokgöz completed two Ph.D. degrees, one in Mathematics and
another one in Industrial Engineering, at the University of Oklahoma along
with a master’s degree in Computer Science and two master’s degrees in
Mathematics. Due to his interest in biomedical engineering applications of
mathematics and engineering, he pursued an online biomedical engineering
master’s degree for professionals at Johns Hopkins University. His other
research interests include nonlinear optimization, game theory,
deep/machine learning, financial engineering, facility allocation problems,
vehicle routing problems, systems’ design and improvement, network
theory and analysis, inventory systems, and Riemannian geometry.
https://t.me/medicina_free
Marina A. Carro is a second-year medical student at the Frank H. Netter
School of Medicine (Quinnipiac University). Prior to medical school, she
worked as a project manager at Clínica Esperanza Hope Clinic in
Providence, Rhode Island, where she organized and managed a satellite
COVID-19 vaccination clinic for underserved populations in the area.
Additionally, she has worked as a certified nursing assistant for 3 years in
the emergency department and intensive care unit at South County Hospital
in Kingston, Rhode Island. Currently, she is on the board for the Frank H.
Netter Wellness Committee, ENT Surgical Interest Group, and American
Medical Student Association at Netter. She hopes to continue exploring her
interests in clinical procedural research, healthcare business and
administration, and provide equitable healthcare for marginalized patients
throughout the rest of her career.
1 Introduction
Development of algorithms allowing to make informed decisions based on
patterns learned from data and mimicking human behavior by using
technology has been one of the goals of researchers for real-life applications
[70]. Machine learning (ML) algorithms are computer methods that can be
trained to find characteristic features and patterns in data. The idea of
artificial neural network (ANN) development follows the same footsteps of
the physiological neural networks. A multilayer perceptron is an artificial
neural network consisting of an input layer, an output layer, and several
hidden layers between the input and output layers. Neurons are the building
blocks in between each one of these layers that serve as computational
building blocks of the network. Neurons are interconnected in subsequent
layers, and output of each neuron is the product of its input with a learned
set of weights that is added to a learned bias. An activation function is used
for deciding whether a neuron should be activated or not. Training, testing,
and validation are three steps that can be used as part of a model
development in ANN. The training phase allows the model to
determine/learn the weights and biases of the ANN from training data.
ANN is expected to propagate a large amount of input data to properly
predict the values of the output layer during the training process. The size
https://t.me/medicina_free
of the data has an impact on the attained results. The differences between
the predictions of the ANN and the desired output values are targeted to be
minimized iteratively by updating the weights and biases of the network
during training. A model that works at the best level is determined on the
training test with the goal of using on the testing set. Training and testing
sets are chosen independently for unbiasing the model’s accuracy. The
validation phase is used to ensure that the developed ANN didn’t memorize
the training data characteristics by attaining good results on other datasets
to validate its effectiveness of application on multiple settings. ANNs can
be used to automatically perform specific tasks, such as predictive
outcomes after optimizing the trainable parameters attained.
Facial traumas that have been seen in emergency departments include
traffic accidents, assault, and fall overs [72–74]. There can be several
reasons for a plastic surgery, and AI applications can be possible that
associate with these reasons that include, but not limited to, the following:
Fracture repair
Nerve paralysis/rehabilitation
Restoration and reconstruction
Septal deviation and reconstruction
Obstruction, scar revisions, and anomalies
Cancer reconstruction
Unidentified or untreated fractured facial bones have an important place
in an individual’s health; such fractures that are not treated properly can
result in complications and sequelae such as nasal canal rupture and eyeball
retraction. Manual methods used for such detection are good; however, AI
solutions can improve such detections through advance analytics.
Face recognition techniques have been developed by using a variety of
techniques including artificial neural network (ANN) approach in deep
learning as well as linear binary patterns histogram, local feature analysis,
local binary patterns, speeded-up robust features, geometric features,
principal component analysis, eigenface, and Fisher vector analysis [75].
Some of the optimization methods used included evolutionary-genetic
algorithm (GA), particle swarm optimization (PSO) and structural similarity
image maps (SSIM) that aimed to for good precision of the recognition.
Deep learning is implemented in a variety of facial plastic surgical
applications, including, but not limited to, aesthetic surgeons, to assess
https://t.me/medicina_free
gender typing following facial feminization, and perceived age changes
following rhinoplasty and facelift [81–84]. ANN is utilized as early as 1991
in [80] to distinguish male and female faces. Deep convolutional neural
networks (CNN) are determined to be powerful tools for face detection and
gender classification. Recently, a facial recognition software called
DeepGestalt (FDNA, Inc., Boston, Mass.) is shown to be superior in the
recognition of known genetic syndromes based on facial features to the
capabilities of clinical experts [81]. Research on surgically altered face
recognition and face feature reading by using a variety of DL techniques
has been also studied extensively [84–89].
The next section focuses on AI/ML/DL applications on facial feature
recognition, which is determined to be one of the popular areas of interest
in the research community.
2 Facial Feature Recognition by Using Machine
Learning Applications for Facial Plastic Surgeries
Recent advancements in technology are utilized to analyze facial plastic
surgical outcomes. Analysis of facial structure for pathological or
dysmorphic issues is a typical part of plastic surgical procedures given that
it may be a reconstructive or an aesthetic surgery. Computed tomography
(CT) cephalometric radiography is used for 3D reconstruction with the help
of computer programming and advancements in image quality [20–24];
however, these applications are expensive with limited access to such
technology. Rapid progress on technological advancement of smartphone
applications is reflected onto facial recognition–related analysis after plastic
surgeries. Such applications are rare to see in practice, and further
development on these applications is limited to the developers’ updates on
the applications [28]. Machine learning algorithms are used for craniofacial
dysmorphology analysis, which focuses on diagnosis and measurement
analysis [25–27]. Integration of machine learning and cellular-based
technology is one way to advance plastic surgical outcome analysis.
Regarding facial landmark recognition, ML Kit by Google is an opensource application programing interface (API) capable of real-time facial
landmark recognition and provides extensive number of facial coordinate
points [19]. In the case when such an automation is not utilized, human
https://t.me/medicina_free
Соседние файлы в папке Библиотека им академика М.И. Перельмана