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Brief Introduction toArticial Intelligence andMachine Learning
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prostate classication at MRI [11], breast density at mammography [12], COVID-19
detection [13], and ventricle segmentation at cardiac MRI [14].
In this chapter, following a brief history of AI, and machine learning, we introduced how machine learning models work. Furthermore, we show how they can be
applied in clinical studies.
2 A Brief History ofArtificial Intelligence
In 1955 John McCarthy, an assistant professor at Dartmouth College at the time,
and his colleagues coined the term “articial intelligence” in their paper proposal
submitted to a summer conference that was soon to be held at Dartmouth [15].
Although this event is believed to be the birth of AI as a distinguished subject, initially it was Alan Turing, an English mathematician and computer scientist, who in
his paper “On Computable Numbers” in 1936 proposed the concept of a computer
[16] and then 14 years later in a 1950 paper titled “Computing Machinery and
Intelligence” set the foundation of one of the rst intelligent machines [17]. For his
pioneering efforts and contributions to this matter, he is now known as the “Father
of AI” [18].
However, if we consider AI as a mighty tree grown from a seed that Turing
planted and nurtured to a seedling by McCarthy and his collaborators, perhaps it
is safe to say that Aristotle plowed the eld thousands of years before. The analysis of humans’ train of thoughts and how they perceive their surrounding world,
discussed in Aristotle’s logic, was the philosophical foundation of what we now
know as “computing” [19]; if we can identify a human brain’s method of thinking, then perhaps we can create an “articial” brain that can mimic human
intelligence.
Therefore, all different denitions that have been presented to explain AI derive
from one similar concept: systems that can solve a problem by simulating the process of thinking and deduction [20]. McCarthy etal. wrote in their proposal:
The study is to proceed based on the conjecture that every aspect of learning or any other
feature of intelligence can in principle be so precisely described that a machine can be made
to simulate it. An attempt will be made to nd how to make machines use language, form
abstractions and concepts, solve kinds of problems now reserved for humans, and improve
themselves. [21]
After the Dartmouth summer conference, AI became one of the most popular
elds in technology. Many books and papers were published concerning various
types of intelligent systems. Two main theories were discussed in the AI eld: sym-
bolic AI and connectionist AI [22].
The concept of symbolic AI is centered around the idea of encoding human
knowledge into a system so that it can solve a problem based on the information
given and conducting if-then-else rules. This school of thought came from
McCarthy’s hypothesis, which was previously mentioned. On the other hand, Frank
Rosenblatt introduced the concept of what we now know as connectionism. He
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proposed that an intelligent system should be able to function like a brain: via neural
networks (NNs). Over time, this system can recognize specic traits or patterns
from analyzing the input data and, unlike symbolic systems, does not solve the
problems solely based on preprogramming or traditional computer logic [23].
Rosenblatt named the articial neuron “perceptron.”
S. R. Motamedian et al.
2.1 Artificial Intelligence andMedicine
Soon, AI found its way into medicine. Even though the rst research in this eld
began in the late 1950s, the main breakthroughs occurred two to three decades
later with the development of unique expert systems such as MYCIN [24]—an
intelligent system developed to help clinicians with diagnosis and antibiotics
selection in patients with bacterial infections. An expert system is constructed
through the implementation of symbolic AI algorithms, where the “inference
engine” analyzes the input data according to the information available in the
“knowledge base,” makes a decision, and presents it as the output [18]. Aside
from assisting in diagnosis and therapy, automatic identication of potential
adverse drug events and notication of patients and their healthcare providers of
upcoming monitoring or preventive procedures are other tasks in which expert
systems have aided clinicians [25]. Nowadays, most expert systems and generally
symbolic AI have become obsolete. Instead, the main focus is on systems which
can learn from provided data. In this case, there is no necessity for manually
encoding the base knowledge into the systems. Machine learning is a branch of
AI, dened as the study of developing intelligent systems that can learn by “gathering experience” [26]. In other words, the system receives the input data, analyzes the patterns and structures concerning the desired task, and improves its
performance by learning along the way. NNs, which started with Rosenblatt’s
single neuron (perceptron), are considered a subgroup of machine learning.
Progressions in the eld of deep learning started in the 1980s and more complex
systems were developed by connecting several perceptrons to build a network
[27]. One of the rst applications of NNs in medicine was William G. Baxt’s
research in 1991, which used an articial neural network (ANN) for diagnosing
acute myocardial infarction [28]. Currently, deep learning algorithms are widely
used in the medicinal elds due to their remarkable ability in visual tasks such as
object detection and recognition [29].
2.2 Artificial Intelligence andDentistry
From managing appointments and checkup reminders to diagnostic and treatmentplanning assistants, AI has helped dental clinicians perform simple to complex tasks
with higher efciency; it is faster and more persistent and needs less human resources
[30]. Similar to the rst attempts on automation in medicine, AI made its way into
Brief Introduction toArticial Intelligence andMachine Learning
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dentistry through expert systems. In a review published in 1992, Stheeman etal.
reported ve expert systems in the dental eld [31]. The latest dentistry- related
expert system at the time was “Oral Radiograph Diagnosis” presented by White in
1989 [32]. This program contained information about 97 conditions and was designed
to assist in oral radiograph differential diagnosis using analytical decision-making.
The other four expert systems reviewed in the article were used for treatment planning [33], diagnosis of pulpal disease [34], orthodontic treatment advice [35], and
oral diagnosis [36]. Nowadays, the growing fascination with automation has led to
much research around developing various AI systems—mostly based on machine
learning and, in particular, NNs—to assist oral healthcare providers in different dentistry subelds. Some examples are artifact reduction in low-dose dental cone beam
computed tomography (CBCT) in radiology [37], prognosis prediction of periodontally compromised teeth [38], skeletal age determination [39], caries detection [40],
working length determination of the root [41], and prediction of bisphosphonaterelated osteonecrosis occurring after tooth extraction [42]. Diagnosing osteoporosis
[43], detecting TMJ osteoarthritis [44], evaluating periapical lesions [45], predicting
oral cancer and locoregional recurrences [46, 47], and determining whether extractions are needed prior to orthodontic treatment [48] are other examples of AI application in the oral and dental health eld.
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3 How Does Machine Learning Work
3.1 The Learning Process
Generally, a machine learning model tries to learn the hidden pattern data itself or
relevance to a label (e.g., reference standard). Regarding the type of learning that
the machine algorithm is working on (and how it is trained), four main subtypes
exist: (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) reinforcement
learning (Fig.2). A supervised learning algorithm relies on labeled data during the
training phase. The algorithm is usually validated using assumptions that are based
on human expertise. Typically, the algorithm tries to learn an input-output mapping
so that inputs can be handled and outputs can be generated. Machine learning refers
to “ground truth” as considered accurate and authentic data. Learning algorithms in
unsupervised learning do not receive specied labels regarding the data. Data are
clustered or grouped when machine learning is used to discover hidden patterns in
the data. Semi-supervised learning combines both unsupervised and supervised
techniques by combining labeled and unlabeled data.
Considering reinforcement learning, machines and computers carry out tasks
and receive negative or positive feedback regarding the reinforcement process.
Explicit instruction is not necessary to learn from reinforcement. Learning occurs as
a result of interactions with the environment. Machine learning algorithms might be
a mixed approach consisting of supervised, unsupervised, and reinforcement learning [49–56].
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S. R. Motamedian et al.
Supervised Learning
Training
Set
Dog
Labels
Unsupervised Learning
Training
Set
Semi-supervised Learning
Training
Set
Dog
Unlabeled
Reinforcement Learning
Cat
Unupervised
Cat
Reinforcement
Deep Model
Model
Test Set
Semi-Supervised
Model
Actions
Rewards
Supervised
Cluster 1
Test Set
Environment
Model
Test Set
Dog
Cluster 2
Cat
Run?
Jump?
Fig. 2 Various types of supervisions in machine learning and their learning process
This is a Dog
This belongs
to cluster 1
This is a Dog
3.2 What Can WeDo Using Machine Learning?
Six main applications of deep learning in the context of healthcare are listed below
(Fig.3):
1. Image processing: techniques applied either on raw signals or reconstructed
images (e.g., noise and artifact reduction) [57].
2. Object detection: identifying a particular subregion in an image, using bounding
boxes, expected to contain a specic object, for example, detection of brain
tumors in magnetic resonance imaging [58].
3. Segmentation: extraction (or delineation) of a lesion or organ of interest based
on image analysis (e.g., pixel intensity, texture, edges), for example, vessel segmentation in coronary arteries to detect probable stenosis [59].
4. Classication: binary or multi-class categorization of a group or type from one
class to another. For example, wound classication in patients with chronic
wounds is crucial for diagnosis that can be automated using deep learning [60].
5. Prediction: anticipating the progression of pathology. For example, in patients
who have cancer, this task may include predicting response to chemotherapy and
the recurrence-free disease prognosis in treated patients [61].
6. Landmark detection: identifying desired landmarks on medical images. For
example, cephalometry landmark detection using deep learning algorithms
showed a favorable performance in the literature [29].
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Image Processing Object Detection
Segmentation Classficiation
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Dog?
Cat?
Prediction Landmark Detection
Fig. 3 Various machine learning tasks that can be formulated as a medical problem for an articial
intelligence-based approach
Dog
Cat
3.3 Data Collection andPreparation
Data preparation, including curation, de-identication, and labeling images for AI
algorithm development, is necessary for having reliable outcomes. There are main
steps that need to be taken to use medical image data in an AI algorithm. The rst is
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local ethical committee approval by the institutional review board. Retrospective
studies do not require the patients’ written consent, and additional procedures are
not necessary. However, in prospective studies, informed consent is required. After
ethical approval, data needs to be accurately de-identied and securely stored.
Structuring the data in homogenized and machine-readable formats is the next vital
step to be done [62]. The nal step is to associate the images with provided ground
truth information, which can be one or more labels.
S. R. Motamedian et al.
3.3.1 Data Collection andCuration
Data collection and curation are steps performed to standardize and improve dataset
quality for training deep NNs. Exploring and cleaning data is the most time-consuming step in an AI project but is critical to any model training. By following
general guidelines and human supervision, one can achieve high-quality standards
in datasets building [63]. Data exploration evaluates the initial raw dataset’s general
qualitative or quantitative properties to determine special features, global trends, or
outliers.
3.3.2 De-identification
Due to the importance of patient-related privacy information, three concepts of deidentication, anonymization, and pseudonymization should be paid attention to.
By de-identication, patient-related information will be masked from individual
records [64]. However, by anonymization, the preferred approach for sharing medical data, patient-related information from individual records will be removed irreversibly. In pseudonymization, patient- related information will be substituted with
synthetic values so that the original data can only be accessed with a predened
secure key [65]. In addition to pixel information, digital imaging and communications in medicine les need to be anonymized following protected health information regulations [66].
3.3.3 Resampling Medical Images
Medical images’ spatial resolution is generally higher than other image types.
Besides, many medical image studies are three-dimensional instead of two-dimensional. So, computers with strong computational power are inevitable. Solutions to
these problems are downsampling of the image resolution and patch-based analysis
of image parts with relevant information.
3.3.4 Data Labeling andReference Standard
After selecting appropriate images, clinicians will classify samples, put landmarks,
or delineate the region of interest through bounding boxes or segmentation masks.
The reference standard, also identied as “ground truth,” must be dened before the
training process. The term ground truth indicates information acquired from direct
observation. Setting the reference standard may vary depending on the project.
There are four approaches suggested:
1. Using gold standard: Consider we wanted to classify oral lesions based on intra-
oral photography to malignant and benign lesions. In this case, we can use histo-
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pathology results for the reference standard. However, this approach is not
applicable in most studies.
2. Consensus: A group of clinicians reaches a consensus on a diagnosis.
3. Major voting: A group of clinicians vote for the diagnosis. The label with the
most vote will be set as the reference standard.
4. The intersection of areas (only for segmentation studies): Two or more clinicians
draw segmentation masks, independently. Pixel-wise intersection of the segmentation mask area will be considered as the reference standard.
Various tools have been introduced to both the research and industry community.
LabelMe and ITK-SNAP are suggested for 2D and 3D images, respectively. Both of
these tools are open-source and free.
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3.3.5 Dataset Sampling Strategies
The ability of an algorithm to achieve satisfactory outcomes on unseen data is
called generalization. The entire dataset needs to be divided into different sets to
increase generalization. The samples in all groups should be independent of each
other and identically distributed. The most adapted sampling strategy in deep
learning is dividing the dataset into training, validation, and test sets. The optimal
ratio varies in different situations, but as a rule of thumb, the proportion of 80%
train, 10% validation, and 10% test division is suitable. Using the validation set,
optimal hyperparameters are selected. Ultimately, the algorithm is used once on
the test set to measure nal performance. k-fold cross-validation is another commonly used sampling strategy used in small datasets. Data is split to k equally
folds. The algorithm is trained on n-1 folds in each training step and tested on the
left fold. The mean of this repeated k-times process gives the nal performance.
However, this sampling strategy cannot be deployed in a large dataset because of
computational cost.
3.3.6 Challenges ofSmall Datasets
The main challenge in applying deep learning in the clinical eld is inadequate
data samples since small sample sizes hinder generalizing the model to unknown
data [67]. Since building large medical datasets of excellent quality is challenging and costly, some innovative training strategies and model architectures have
been proposed, such as weak labeling [68] or few shots learning [69] to address
these limitations, especially in the medical eld. However, the primary approach
to confronting this challenge is using transfer learning. Transfer learning is
related to applying an earlier-learned approach from a task to another but a similar project. Using transfer learning, we can use labeled data from one set of tasks
to do another. It provides a promising opportunity for machine learning to
advance rapidly in different domains. For instance, a convolutional neural network (CNN) for classication and pattern recognition of nonmedical images on
ImageNet (http://www.image- net.org) has been applied to chest CT scans to
extract their features and predict the survival rate of lung tumors [70, 71].
Accordingly, medical image analysis can benet from knowledge gained from
nonmedical image analysis.
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S. R. Motamedian et al.
3.4 Model Implementation
3.4.1 Machine Learning Models
There are various numbers of machine learning models used to solve different problems. Here, we briey introduce some of the most important ones (Fig.4).
Linear Regression
The linear regression technique is probably one of the most widely used and comprehensive statistical and machine learning methods. When looking for a linear relationship between two variables, linear regression is often utilized. Using linear
X
+
Y
Fig. 4 Selected machine learning techniques. (a) Logistic regression. The likelihood of a binary
result is modeled using logistic regression. The Y axis in the image indicates probability, whereas
the X axis represents the continuous input variable. Take note that minor changes in X result in
huge differences in the nal probability Y, particularly in the middle region of the gure, where the
model’s uncertainty is greater. This approach may be extended to issues with many classes. (b)
Support vector machine. This model may convert a nonlinear border to a linear one. The training
procedure maximizes the distance between classes and the nal chosen border. (c) Random forest.
Random forest is a strategy for minimizing the uncertainty in the nal forecast by combining decision trees. It is based on a recursive binary splitting technique in which the higher nodes are meant
to be the most discriminative and subsequent branching is applied to variables that are less important. (d) Clustering. Clustering is a method that enables the identication of subgroups (clusters)
within a set of data. There are several clustering algorithms; some need a predened number of
clusters, others may be employed with output data, and still others are completely unsupervised
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regression, there are two options: single and multiple. A model with a single independent variable is known as a simple linear regression [72]. Using a simple linear
regression, we can see how a variable varies. Simplied regression separates the
effects of independent variables from the interactions of dependent variables [73].
Multivariate linear regression (MLR) is a statistical approach that uses several
explanatory factors to predict the outcome of an answer variable. The purpose of
MLR is to simulate the linear connection between the independent variables x
and y [74].
Logistic Regression
Contrary to a binary classier, logistic regression (LR) models rely on cumulative
input variables to estimate their degree of predictability. This model can have a
disproportionate impact on nal probability predictions in a slight change in the
input value. Furthermore, the number of predictor variables should be limited since
it might cause escalating training process, overtting, and consequent weak model
robustness. The LR model is a suitable alternative for binary classication of less
complex data.
Support Vector Machine
Support vector machines (SVMs) employ supervised machine learning techniques
to divide data into distinct categories by determining the optimal boundary. Gaussian
function (or linear function) is the most commonly utilized kernel. Regarding the
pros and cons, SVM models need vast storage when applied to a high amount of
data; however, SNM would be a model of choice to identify nonlinearity and sparsity in the data.
Random Forest
A random forest (RF) model is an ensemble of decision trees (DTs) trained on random samples taken from a training set. Decision trees are a set of explicit rules
adjusted for classifying various features of the training set. Moreover, DTs are
known as high variance nonlinear models. Accordingly, very deep and accurate DTs
tend to reveal irregularities and overtting issues. Consequently, applying the RF
model to several datasets may solve this issue. Therefore, the model is getting more
generalized and more applicable. The mode for classication goals and the mean for
regression analysis goals are the most optimal choice of obtained predictions.
Notably, two main parameters should be considered: (1) the number of applied decision trees and (2) the depth level of each decision tree. It is worth mentioning that
the more depth levels, the more inuential the model would be, and in contrast, the
less generalized the model would be.
Cluster Analysis
Cluster analysis is dened as grouping data points based on similarities and proximities in the quantitative analysis of variables extracted from input data to obtain
the space of the parameters. Cluster analysis may help in patient stratication (e.g.,
based on pathology images). Moreover, k-nearest neighbors (kNN) is the most
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S. R. Motamedian et al.
common clustering model used for classication in a supervised way. In nding
subgroups, k is the number of neighbors to be considered. Accordingly, predictions
for a new patient are based on its surroundings.
3.4.2 Deep Learning Models
Various structures have been suggested for deep learning models, mostly based on
ANN.Here, we briey introduce some of the most well-known structures and models (Fig.5).
Artificial Neural Networks
Biological NNs inspire ANN patterns. ANN models generate predictions from input
data in a hierarchical way, using nodes and layers. An ANN can adapt until it is
found that the weights (i.e., input lines) give the best performance. In the case of
large amounts of data, ANNs are the best choice. In this section, we present basic
neural network architectures.
Convolutional Neural Network (CNN)
CNNs are the most common deep learning algorithms used for image analysis.
CNNs have shown successful performance measures regarding various image
analysis tasks (e.g., detection, classication, segmentation). As mentioned above,
hidden layers are a part of CNNs, which are functional layers (i.e., convolutional
layers, pooling layers, and/or fully connected layers) converting the input data to
output. In simple NNs, the neurons of one specic layer are connected to every
neuron of the previous layer; thus, the layer is considered fully connected. However,
a deep NN architecture consisting of fully connected layers is very computationally expensive, and tting a large number of parameters is challenging [2]. In
CNNs, the neurons are only locally connected. The pattern used to calculate the
connections for one neuron is reused over all neurons of the same layer [75].
Therefore, CNN has drastically fewer parameters than a fully connected deep NN
of the same size.
Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are another subset of NNs mainly used for
sequential data (e.g., MRI and ultrasound image sequences). RNNs can “remember” the past and use the knowledge derived from the past in making current decisions. Considering the function of RNNs, the rst image is used as an input in this
method, and predictions are made based on that. Further, RNNs memorize the previous predictions and predict the following image sequence. RNNs widely contribute to model long-term memory. In this regard, LTSM [76] and gated recurrent unit
(GRU) [77] are the most popular.
Generative Adversarial Nets (GANs)
Goodfellow etal. [78] advocated using a generative adversarial network (GAN) for
picture synthesis from noise. Generative articial neural networks (GANs) learn to
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