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Brief Introduction toArticial Intelligence andMachine Learning
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a
Dog
Cat
Discriminator
Training Set
279
Noise
Generator
Fake
image
Fake ?
Real ?
Fig. 5 (a) Articial neural networks. By propagating structured data (green nodes—input vari-
ables), such as radiomics, through hidden layers (left brown nodes), articial neural networks can
model complex nonlinear relationships between input variables and outcomes (right brown nodes).
(b) Convolutional neural networks. Deep learning applications rely on convolutional neural networks as their backbone. They are composed of input and output layers that are separated by a
number of hidden layers. (c) Recurrent neural network. This type of articial neural network is
characterized by connections between nodes in a sequence. This model can process variable-length
input sequences by utilizing their memory. (d) Generative adversarial networks. Generally, these
models are used for generative modeling, which involves automatically identifying regularities or
patterns in input data to produce new examples that can be used as a replacement for the original
dataset. (e) Autoencoders. These structures are neural networks that learn efcient data representations (encoding) by training. They can be used for denoising images, compressing images, and
generating images as well
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mimic the distribution of real-world data and may thus generate new patterns of
visual data (images). Both the generating network and the discriminator network are
part of the GAN.As part of their training, the discriminator and generator compete
against one another to generate convincing false pictures that trick the discriminator
into thinking the images are genuine. Since both models have a stake in the competition’s outcome, this training method is called “adversarial training.” This kind of
training may also be applied to developing a network segmentation. To differentiate
between the produced and ground truth segmentation maps, we use a segmentation
network instead of a generator (the target segmentation maps). This promotes the
segmentation network to create segmentation maps that are more physically realistic [79, 80].
Autoencoders (AE)
Autoencoders (AEs) are a subclass of NNs meant to learn compressed implicit representations from the input autonomously. An AE’s usual design is composed of an
encoder network and a decoder network for reconstructing the input. Numerous
CNN variations have been suggested to transmit information from the encoder to
the decoder and improve segmentation accuracy. The U-Net is the most well-known
CNN version for biomedical image segmentation [81]. The U-Net utilizes “skip
connections” between encoder and decoder for better segmentation to restore lost
spatial information during the downsampling process.
S. R. Motamedian et al.
3.5 Requirements forTraining theModel
3.5.1 Software Requirements
Various programming languages can be used for deploying and implementing the
models. However, the Python programming language is more favorable among scientic communities and industrial applications. Deep learning libraries provide a
higher level of the programming interface, including many levels of mathematical
concepts based on linear algebra, calculus, probability, and numerical computation
to efciently use available computational resources like the GPU or CPU [82].
TensorFlow [83] and PyTorch [84] were the two most used libraries in 2021.
Frequently used, these frameworks can implement NN architectures.
3.5.2 Hardware Requirements
Hardware selection, which means determining the technical specs based on a given
deep learning model, is essential in running a deep learning project. Dataset volume
and model complexity are two key parameters to consider in hardware selection.
CPU, GPUs, or cloud computing platforms can be used for training deep learning
models. GPUs and TPUs, unlike CPUs, are architectures designed for heavy parallel computing, with limited memory size but at very high bandwidth. Using more
signicant GPU memory (currently, commercial GPUs provide memory sizes
between 8 and 32GB), training deeper models with a higher number of trainable
parameters will be convenient [85]. To increase computational performance, one
Brief Introduction toArticial Intelligence andMachine Learning
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281
can use multi-GPU with considering additional hardware setups (e.g., power supply
and cooling).
Cloud computing, which refers to internet-based services using a third-party
hardware resource, can be the rst decision if training time matters. Although cloud
computing platforms provide high computational power with unlimited storage,
they suffer from specic shortcomings such as technical issues caused by data fractioning and data security. In this context, de-identication and patient anonymization concepts are of paramount importance that should be considered [86].
3.6 Model Evaluation
It is crucial to dene precise metrics to evaluate task performance in training deep
learning models. Commonly used metrics in the classication tasks are accuracy,
sensitivity, specicity, precision, and recall. F1 score combines precision and
sensitivity.
The metrics are mainly used in detection and segmentation tasks to assess the
similarity of an automatically created bounding box or a segmentation mask to previously associated ground truth. Two common metrics are intersection over union
(IOU) and Dice or Jaccard coefcients. IOU is measured by dividing the area delimited by the intersection of two bounding boxes and the union of the same two bounding boxes.
4 Conclusion
AI, and more specically machine learning, is revolutionizing healthcare and medical care. Compared to other real-world problems, the progress of machine learning
approach’s application is relatively slow in healthcare. This chapter introduced how
a machine learning algorithm works and its requirements. This will provide
researchers and clinicians basic understanding of these models for using them in
research and the industry.
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Application ofArtificial Intelligence
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inDiagnosing Oral andMaxillofacial
Lesions, Facial Corrective Surgeries,
andMaxillofacial Reconstructive
Procedures
ParisaMotie, GhazalHemmati, ParhamHazrati,
MasihLazar, FatemehAghajaniVarzaneh,
HosseinMohammad-Rahimi, MohsenGolkar,
andSaeedRezaMotamedian
P. Motie
DDS, Medical Image and Signal Processing Research Center,
Isfahan University of Medical Sciences, Isfahan, Iran
G. Hemmati
Dental Research Center, Research Institute of Dental Science, Shahid Beheshti University of
Medical Sciences, Tehran, Iran
P. Hazrati
Student Research Committee, School of Dentistry, Shahid Beheshti University of Medical
Sciences, Tehran, Iran
M. Lazar
Shahid Beheshti University of Medical Sciences, Tehran, Iran
F. A. Varzaneh
Student Research Committee, School of Dentistry, Shahid Beheshti University of Medical
Sciences, Tehran, Iran
H. Mohammad-Rahimi
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health,
Berlin, Germany
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
M. Golkar
Oral and Maxillofacial Surgery Resident, Dental School, Shahid Beheshti University of
Medical Sciences, Tehran, Iran
S. R. Motamedian (
Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health,
Berlin, Germany
Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid
Beheshti University of Medical Science, Tehran, Iran
e-mail: drmotamedian@gmail.com
*)
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
A. Khojasteh et al. (eds.), Emerging Technologies in Oral and Maxillofacial
Surgery, https://doi.org/10.1007/978-981-19-8602-4_15
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P. Motie et al.
1 Introduction
The term articial intelligence (AI) refers to the use of computers to simulate intelligent behavior with minimal human input [1]. General healthcare delivery can be
achieved with two types of AI: physical and virtual. Sophisticated robots or automated robotic arms represent physical applications and software-type algorithms
are virtual components that can be used to support clinical decision-making [2].
Rapid developments of technologies in the medical imaging eld prompt a large
amount of visual data. The collection, analysis, and application of such vast amounts
of data have posed challenges for modern medicine in solving clinical problems.
The leading strategy to overcome this complexity is employing medical articial
intelligence, nonhuman intelligence systems which are designed to assist clinicians
with the formulation of diagnosis, therapeutic decision-making, and predicting
treatment outcomes [3].
CNN is a subcategory of the deep learning (DL) models. The special construction of CNN has made it a superior model for image processing; the neurons’ links
signicantly reduce the computational overload. CNN can instantly learn and identify the images’ patterns. It can also detect, classify, and segment objects [4, 5].
“Classication” ability of CNN models can be used in categorizing target lesions in
radiographs. “Segmentation” of the anatomic landmarks is one of the essential
image processing steps. In the former AI models, segmentation had to be done manually by a radiologist. In contrast, CNN can automatically segment organs or lesions
and dramatically reduce the burden of clinician work. The self-regulated “detection” task of CNN makes large-scale health screenings more enforceable. Kooi etal.
conducted a study to evaluate the performance of CNN models in large-scale
(45,000) mammographic screening. There was no signicant difference between
CNN and specialized radiologist readers [6, 7].
2 AI Applications inRecent Medicine
2.1 AI inRadiology
The base of medical radiology is extracting the images’ essential features. In the
recent years, the DL pattern extraction potential has pitched in, saving up a vast
amount of time by automating the image analysis [8, 9]. According to a recent study
on 874 chest radiographies (CXR), the DL algorithm might be a helpful tool in
interpreting CXR ndings such as pulmonary opacities, hilar prominence, cardiomegaly, and pleural effusion. In addition, changes or stability of these ndings can
be evaluated on follow-up CXR.It was also concluded that clinical staff might be
replaced with AI if they are unavailable [10]. In detecting COVID-19, DL is one of
the benecial tools for triaging patients and differentiating them from other types of
pneumonia based on CXR [11–13]. Moreover, DL can help the clinicians in the
detection of pulmonary nodules [14–17], lung cancer [16, 18, 19], pneumonia and
Application of Articial Intelligence in Diagnosing Oral and Maxillofacial Lesions…
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distinguishing bacterial from viral one [20–22], referable thoracic abnormalities
[23], pulmonary tuberculosis (PTB) [24, 25], and pneumoconiosis [26].
Besides the respiratory system, DL can also assist clinicians in other elds like
bone age assessment out of wrist and hand radiographs [27], predicting the presence
of coronary artery calcium (CAC) correlated with the risk of cardiovascular disease
[28], and indicating vertebral fracture [29].
289
2.2 AI inOncology
ExPecto, a DL-based framework, is organized to accurately predict tissue-specic
transcriptional effects of mutations from DNA sequences, even those that are rare or
not seen yet; therefore, this algorithm makes it possible to predict the risk of expression and mutation disease effects [30]. The Watson for Oncology (WFO) AI system
is designed to support clinicians in planning treatment for breast cancer [31]. In
detecting lymph node metastasis in women with breast cancer, another DL algorithm proved better diagnostic performance than 11 pathologists [32]. DL can also
help the cancer team supply clinical suggestions for colorectal cancer [33]. Nasal
basal cell carcinoma is a complex disorder that needs a multidisciplinary team
(MDT) for treatment and patients should be triaged for Mohs micrographic surgery
(MMS). Machine learning approach is a helpful tool guiding the MDT with patient
selection and predicting the need of MMS, which reduces the waste of time and
nancial burden and improves patient care [34].
2.3 AI inOphthalmology
Diabetic retinopathy (DR) is one of the most common preventable causes of blindness in the world. The AI algorithm can recognize the cases that should be referred
to the ophthalmologists with high reliability [35, 36]. Patients suffering from agerelated macular degeneration (AMD) experience a loss of vision in the center of the
visual eld because of damage to the retina [37]. It’s been reported that AI can
detect the referable AMD patients and can perform as well as human experts in its
management and risk assessment [38–42]. AI also has a comparable performance to
trained specialists in the detection of retinopathy of prematurity [43–45] and glaucoma [46–48].
2.4 AI inCardiology
In 1985, Willems etal. took the study in which they compared the ECG interpretability of 9 electrocardiographic computer programs with 8 cardiologists in 1220
cases of various cardiac disorders. They gured out that some of the programs, the
best once for sure, have the same interpretation capacity as the cardiologist in seven
major cardiac diseases, including left ventricular hypertrophy, right ventricular
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