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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана
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compared to other occupations, it was found to cause higher pain in the neck and
back. Specically, it was linked to higher levels of prolonged low-intensity muscle
activity, less rest time for the trapezius muscle, and more time spent doing nonsurgical activities and the two functions in RAS.Patient activation was above average
during open surgery.
For robot-assisted surgery (RAS), Peng etal. [28] suggested an endoscope (FOV)
autonomous tracking method that takes into account pose control, and image denition, allowing the AEHR system to reliably and swiftly follow, which incorporates
robot-assisted endoscope holding, was developed in light of clinical needs and the
limits of conventional research. Second, limitations on the RCM of AEHRs and the
corresponding kinematic model equations are constructed. After using deep learning to segment and nd tool tips, an intelligent adjustment control model for the
surgical FOV is constructed, complete with a wide variety of indices. The project
concludes with the development of a prototype system for experimenting with
AEHRs in conjunction with human discourse.
R. S. R. Somula et al.
3 Methods
In order to model a robotic surgical job for estimate purposes, we rst establish a
surgical estimation nite state model (FSM), and then we create the surgical HFSM,
which models a full RAS operation, an inoperable estimate. Due to the complexity
and great dynamic range of the real-world RAS situation, the FSM model does not
impose any necessary constraints on the probability of any state transitions. Figure1
is a block schematic of the suggested model.
Fuzzy Sets and Membership Function
Fuzzy sets and membership functions are concepts from fuzzy logic, a mathematical framework used to handle uncertainty and vagueness in data and decisionmaking. Fuzzy logic is particularly useful in cases where traditional binary logic
(true/false) is inadequate due to the presence of intermediate or partially true values.
In traditional fuzzy set theory, a fuzzy set is dened by a membership function that
assigns a membership degree (a value between 0 and 1) to each element of the universe of discourse. The membership function determines the degree of belongingness of each element to the fuzzy set.
Hierarchy Construction
The HFSM extends the concept of fuzzy sets to a hierarchical structure. The rst
step is to construct a hierarchy of fuzzy sets, organized in a tree-like structure. Each
node in the hierarchy represents a fuzzy set at a specic level of abstraction or
granularity. The root node represents the most general fuzzy set, and subsequent
levels represent more specic and detailed fuzzy sets.
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Fig. 1 Decay, transfer, and compose (DTC) perfect for the nding of surgical state estimation
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Multilevel Membership
In HFSM, an element can belong to multiple hierarchical fuzzy sets simultaneously.
This is in contrast to traditional fuzzy sets, where an element can belong to only one
fuzzy set. Each hierarchical fuzzy set at different levels has its membership function
that determines the degree of membership for an element at that level.
Membership Degree Propagation
One of the key features of HFSM is the ability to propagate membership degrees
from parent nodes to child nodes in the hierarchy. This means that the membership
degree of an element in a child fuzzy set is inuenced by its membership in the parent fuzzy set. This allows for a more exible and accurate representation of complex
relationships between elements.
Aggregation and Composition
HFSM allows for the aggregation and composition of membership values from different levels of the hierarchy. This process combines membership degrees from parent and child nodes to obtain a more comprehensive representation of an element’s
membership in the overall system. Various aggregation and composition methods
can be used depending on the specic application.
Granularity Control
HFSM provides the ability to control the level of granularity at which the data is
analyzed. By selecting different levels in the hierarchy, users can focus on different
levels of detail and abstraction, making the model adaptable to various analysis
requirements.
Applications
The hierarchical fuzzy sets model nds applications in various domains such as
decision recognition, information retrieval, and expert systems. It is particularly
useful in handling complex and uncertain data where multiple levels of abstraction
need to be considered.
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Handling uncertainty: Fuzzy logic allows for the manipulation of uncertain or
imprecise information, which is common in real-world scenarios.
Human-like reasoning: Fuzzy logic can mimic human reasoning, which often
involves degrees of truth and vagueness.
Flexibility: Fuzzy logic can be applied in various domains, including control sys-
tems, decision-making, pattern recognition, and articial intelligence,
among others.
Interpretability: The use of linguistic variables and membership functions makes
fuzzy logic models more interpretable and understandable for humans.
Smooth transitions: Fuzzy logic enables smooth transitions between different states,
which can be useful in control systems.
Fuzzy logic and its concepts have found applications in areas such as industrial
control systems, expert systems, robotics, and more, where uncertainty and imprecision are inherent to the problem at hand.
Denition 1 What is the nite state model (FSM) for surgical estimation?
M(S,Σ,F) is consisting of S, an innitely many non-empty state space, the symbols sent into the system, and P, a set of allowed changes among conditions.
Denition 2 Requiring in-patient surgery, a surgical FSM with states that are
potentially other surgical FSMs is called a hierarchical FSM (HFSM). An HFSM
used in surgery has superstates for its states.
R. S. R. Somula et al.
Denition 3 A superstate is the largest possible breakdown of an operation into
discrete stages.
These denitions just scratch the surface of a full (hierarchical) nite state
machine model; the dynamics of transitioning between states is outside the scope of
this chapter. Therefore, an HFSM is analogous to a graph modeling the connections
between levels of organization. In this chapter, we ignore the possible inuence of
banned transitions on estimator performance because our study does not contain
any surgical conditions with such restrictions.
Using an input data stream, the hierarchical surgical state estimator (i.e., DTC)
estimates operations that may include a more complex hierarchy, and we will keep
things basic for now. The next section discusses the structure and instruction of DTC.
3.1 Datasets
Data from 20 actual transabdominal preperitoneal repairs of inguinal hernias done
using da Vinci®Xi surgical systems are included in the HERNIA-20 dataset [11].
There are 12 single-sided and eight double-sided inguinal hernia repairs in
HERNIA-20. The eight steps in the process represent the eight superstates in an
HFSM.We proceeded to subdivide the ve superstates (SS0–SS4) into a total of
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four intermediate states and one nal ne-grained surgical state. Superstates like
this showed up often in HERNIA-20 and are of great therapeutic signicance.
Because repeated states were classied as the similar class in training and assessment of the at approximation techniques that we test against, overlaps of negrained states exist among distinct superstates. Surgical texts and interviews with
practicing surgeons were used to determine (super)state designations. When estimating a state, DTC places no restrictions on the probability of transitions
between states.
A HERNIA-20 operation might take anywhere from 15 to 70min. There are two
types of laparoscopic instruments. A da Vinci endoscope is used to acquire visual
data during an endoscopic procedure. Four universal patient-side manipulators
(USMs) are used to gather the robot kinematics data. Ten system events are recorded:
six are binary of console (endoscope follow, instrument follow, surgeon-side console master clutches, energy pedal) and four are categorical (da Vinci® surgical tool
put on USMs). Because of the thorough anonymization of HERNIA-20 data, the
name of the surgeon(s) performing each surgery is concealed.
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3.2 DTC Method
The approach recommended for identifying HFSM is described in depth here. The
workow and the formalization of the technique are discussed, beginning with an
impression of the architecture and moving nished the various mechanisms of the
approach.
3.2.1 DTC Architecture Impression
DTC model contains three steps. In the rst step, we will use the DTC’s pretrained
CNN perfect as our backbone to train a model to excerpt deep local characteristics
from respectively image. The next step is to use DTC’s class-decomposition layer to
streamline the local structure of the data. In the second step, the training is conducted using a complex gradient descent optimization algorithm. Finally, a class
conguration layer is used to further enhance the nal picture categorization of the
study. Knowledge is transformed from an ImageNet pretrained CNN model and
added. Each class in the picture dataset is broken down into k subclasses in the class
decomposition layer so that they may be processed separately. Next, the class composition module is used to reassemble the subclasses into a concluding classication of the initial picture collection.
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A
nn nm
12
⋯
ll
{}
=−
i
ac
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R. S. R. Somula et al.
3.2.2 Deep Feature Extraction
(Using the HERNIA-20 dataset, a pretrained CNN perfect was adapted and trained
with a shallow-tuning mode. The image feature space was built using the commercially available CNN features of pretrained, which is completed just on the nal
classication layer.) Images have a high dimensionality; therefore, we projected the
high-dimensional feature space into a lower-dimensional space using PCA [29].
This allowed us to remove characteristics that were highly linked. This is a crucial
part of the class decomposition process that leads to more uniform classes, lessens
the framework’s memory footprint, and boosts its overall performance. In transfer
learning, a pretrained CNN model is used as a starting point, and additional layers
are added to the model to ne-tune it for a new task. The large dataset for a general
vision-related task is like image classication on ImageNet. However, the learned
features in the early layers of the pretrained model can be valuable for various vision
tasks, even if the target domain is different.
To leverage transfer learning, you can add new layers, often referred to as “top
layers,” to the pretrained model. These new layers are typically task-specic layers
responsible for adapting the features learned by the pretrained model to the new
task. For example, if you want to use the pretrained model for a different image
classication task, you may add fully connected layers and a new output layer for
the specic number of classes in your target dataset.
3.2.3 Class Decomposition Layer
Now imagine that our eye space is signied by a 2D matrix and L is a class grouping. The letters A and L may be rearranged to form
aa a
11 11 1
aa a
=
21 22 2
⋮⋮
aa a
⋯
⋯
m
Ll
,,l
m
⋮
⋮
,,…
=
1
k2
(1)
The total number of features is m, and there are k classes and n images. Using
k-means clustering [30–33] for class decomposition, we then divided each class into
similar subclasses (or clusters). Here, the squared Euclidean distance (SED) is used
to assign each pattern in the initial class L to the class label of the centroid with the
smallest SED.
n
k
SED
∑
=
where the centers of each group being represented by the letters cj.
j
j
()
∑
i
1
=
1
j
(2)
AALBBC=
()
→=
()
||
al
al
11
21
bl
bl
mc
11
21
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235
After the clustering is complete, a new dataset will be formed by subdividing
each class in L into k equal subclasses (denoted as dataset). Accordingly, a mathematical description of the connection among datasets A and B may be expressed as:
(3)
If A and B each have the same sum of instances and C stores the updated names
of the classes (e.g., C={l_11,l_12,...,l_1k,l_21,l_22,...,l_2k,...l_ck}), then A and B
can be rewritten as:
aa
11 11
aa
21 22
⋮⋮⋮⋮⋮
A
=
aa all
12
nn nm
⋯
⋯
⋮
⋯
bb
11 11
bb
21 22
B
=
⋮⋮⋮⋮⋮
bb bll
nn nm c
12
⋯
⋯
⋮
⋯
m
m
⋮⋮⋮
m
⋮⋮⋮
2
2
1
21
2
(4)
3.2.4 Transfer Learning
It is likely that the various classes will be adequately represented due to the abundance of large-scale annotated picture datasets. This means that the boundaries
between classes that are learnt are more likely to apply to novel data. On the other
hand, the generalization error may grow due to the scarcity of annotated HERNIA-20
data, especially when severely affected than others in terms of size and illustration.
This is due to the possibility of a misalignment between the demographics of the
minority and the majority groups. When tens of millions of parameters need to be
trained, large-scale annotated datasets (such as ImageNet) give viable solutions
through transfer learning.
In the experimental research part, we will look at how we employed and contrasted multiple ImageNet modes for transfer learning. Overtting can arise when
using stochastic gradient descent (SGD) with insufcient training data because of
236
E
n
j
n
()
()
()
=
0
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R. S. R. Somula et al.
the large uctuations in the objective/loss function. Minimizing the objective function E(·) using cross-entropy loss helped in convergence and prevented overtting:
jj
yzx
,
()
=−
1
jj
1111
ynzx yn zx
∑
+−
()
()
j
−
j
(5)
where xj are the training set of input photos, yj are the true labels, and z(·) is the
projected output of a SoftMax function.
3.2.5 Procedural Steps ofDTC Model
After going through the theoretical underpinnings of the DTC model, we will move
on to a demonstration and summary of the DT model’s operational phases in
Algorithm 1.
Algorithm 1 Procedural Steps of DTC
1:Procedure
–Input
–Imagesetdivided into training and testingset
–Ground−truth labels.
–Output
–Predicted labels.
Stage1Class Decomposition:
2. Usean ImageNet pretrainedCNNmodel(e. g., AlexNet)asafeature extractor to
constructadeep feature extractor to constructadeep feature space from input images.
3. ApplyPCAonthe deep features space for dimension reduction.
4. Usereduced feature space of the input images to decompose original classes intoanumber of
decomposed classes.
Stage2Transfer Learning
5. Adapt the nal classication layer of an ImageNet pretrainedCNNmodel to the decomposed
classes.
6. Fine−tune parameter of the adopted pretrainedCNNmodel.
Stage3Class Composition
7. Calculate the predicted labels associated to decomposed classes.
8. Rene the nal classication using error−correction criteria.
4 Results andDiscussions
The recommended model was developed using Python 3.7, NumPy 1.16.2,
NetworkX 2.4, and TensorFlow 2.1.0. The tests were run on a computer with 32GB
of RAM, a seventh-generation Intel Core i5 processor, and an 8GB GeForce RTX
2070 graphics card.
A
TP FP TN FN
+
++ +
Pr
TP FP=+
Recal
+
F
+
..
Explainable AI (XAI)-Based Robot-Assisted Surgical Classication Procedure
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4.1 Evaluating Models
Accuracy used to assess the projected models (F1). Eqs. (6)–(9) are the formula
used to regulate these values.
ccuracy
ecision
12=
Precicion Recall
TP TN
=
TP
TP
l
=
TP FN
Precicion Recall
(6)
(7)
(8)
(9)
Table 1 presents the validation analysis of proposed prototypical with existing
techniques.
Table 1 represents the performance of cross-validation performance and test performance. In this analysis, we computed the different model and proposed method;
the experimental analysis of proposed model delivers the better accuracy and other
performances. For instance, the accuracy of proposed model for CV is 98% and the
same model achieved 92.22% of accuracy for TP. In addition, the nearly 98% of
PRE, REC, and F1 for CV, where the DTC accomplished nearly 92% of PRE, REC,
and F1 for TP. However, the existing techniques achieved nearly 88–96% of accuracy, PRE, REC, and F1 for CV and these techniques achieved nearly 79–89% of
accuracy, PRE, REC, and F1. When compared with all techniques, DT and CNN
models achieved low performance, i.e., nearly 74–76% of accuracy (PRE, REC, and
F1) for CV performance. Figures2, 3, 4 and 5 present the graphical comparison.
Table 2 represents the HERNIA-20’s surgical superstate approximation performance of DTC.In this analysis, we computed the different models such as Trivial,
Table 1 Comparative analysis in terms of cross-validation with test performance
Approaches Cross-validation (CV) presentation Test presentation (TP)
BiLSTM 96.96 97.0 97.0 97.0 90.89 90.9 91.89 91.89
RNN 94.89 94.87 94.87 94.87 88.6 88.61 88.6 88.6
DTC 98.08 98.09 98.08 98.08 92.22 92.23 92.22 92.22
DT 78.95 82.7 78.95 78.34 67.91 68.95 67.91 67.43
CNN 74.76 78.02 74.76 74.03 64.05 66.79 64.05 62.45
LSTM 94.92 94.92 94.92 94.92 90.18 90.18 90.18 90.17
RF 87.51 87.63 87.51 87.5 86.09 86.13 86.09 86.09
SVM 86.15 86.67 86.15 86.1 78.97 79.98 78.97 78.78
ACC PRE REC F1 ACC PRE REC F1
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Fig. 2 Accuracy comparison
R. S. R. Somula et al.
Fig. 3 Precision assessment
ImageNet, Robot kinematics, and raw endoscopic vision with proposed method; the
experimental analysis of proposed model delivers the better result of ned tuned.
Explainable AI (XAI)-Based Robot-Assisted Surgical Classication Procedure
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Fig. 4 Recall assessment
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Fig. 5 F1-score assessment
5 Conclusions
In this study, we offer a straightforward description of a RAS procedure as a multilevel time-varying state-space model (HFSM) used in surgery. A surgical HFSM
has several superstates, each of which is an FSM made up of discrete surgical
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