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Explainable AI forColorectal Cancer Classication
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Table 4 Specicity of DNN model 1 and 2 of the proposed and current methods using datasets 1
and 2, respectively
Dataset 1 Dataset 2
Mean Std Min Max Mean Std Min Max
Un normalized 0.8188 0.0165 0.7835 0.8561 0.9237 0.0076 0.9081 0.9378
Min–max 0.8380 0.0117 0.8169 0.8652 0.8880 0.0964 0.3838 0.9297
MMADN 0.8021 0.0316 0.7322 0.8541 0.8750 0.0136 0.8541 0.9108
Sigmoid 0.8158 0.0179 0.7671 0.8518 0.8714 0.0230 0.7784 0.9081
ZSN 0.8040 0.0178 0.7671 0.8494 0.8724 0.0124 0.8460 0.8919
Sqrt-sum 0.8640 0.0172 0.8353 0.8986 0.9479 0.0060 0.9378 0.9595
Abs-diff 0.8549 0.0216 0.7854 0.8868 0.9137 0.0107 0.8946 0.9351
Sqrt-prod 0.8567 0.0156 0.8146 0.8871 0.9370 0.0076 0.9162 0.9568
Abs-shift 0.8173 0.0155 0.7925 0.8585 0.9296 0.0123 0.8973 0.9541
Combined 0.8510 0.0143 0.8278 0.8800 0.8647 0.0136 0.8405 0.8919
219
It can be observed that both sqrt-sum and combined or feature extension methods, depending on the properties of the underlying dataset, can readjust the data
distribution, improve feature relevance, normalize data, and reduce the number of
outliers. As demonstrated in Ref. [43] on imbalanced activity classes in smart
homes, readjusting data distribution can improve classication performance. The
use of standard deviation of the raw data to compute new data points shifts the dataset into a new feature space where normal values are mapped to a region that is
almost linear, while outliers and extreme values are mapped along the tails of the
new range [19]. This diminishes the effect of both outliers and extreme features.
Based on the Pareto concept, uniformly computing square roots across a dataset
rescales its data points and reduces the effect of extraneous values by a big margin
while slightly reducing the effect of normal values [34]. Summation is used in the
sqrt-sum method as a simple way to avoid producing extraneous intermediate computation values. Combining standard deviation and square root transformation of
data points produce a new method that combines the good properties of the underlying methods and is more robust. Hence, sqrt-sum (the aggregate method) achieves
the best AUC performance on both datasets and improves sensitivity and specicity
of a DNN model. As observed on dataset 2, sensitivity of the method, however, is
negatively affected when data is highly biased toward negative cases.
The combined method that transforms a scalar data point into a vector representation by combing synthetic datapoints produced by sqrt-sum, abs-diff, sqrt-prod,
and abs-shift methods combined with the original scalar data point produces a dataset with an increased feature space. It transforms a two-dimensional to a threedimensional dataset in order to reduce variability based on the concept of noisy
replicates. Adding noisy replicates to a dataset has proven to be a natural regularization technique for classication of models that are based on skewed datasets [44].
As a result, it has been possible to develop data augmentation techniques that draw
from the concept of noisy replicates to improve classication of models based on
microbiome data [23]. As an example, recently Lo and Marculescu [24] proposed a
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method that uses negative binomial distribution to create noisy replicates as way of
augmenting microbiome data in order to improve classication accuracy of a DNNbased model.
In the context of ML classication tasks, a prevalent approach to enhance the
efcacy of a model is to augment the sample size, thereby mitigating the impact of
variability. Alternative techniques, such as feature selection, can be employed to
mitigate variability. Feature selection is a method employed to address overtting,
whereby certain data features are eliminated to decrease the dimensionality of the
input data. The technique is primarily employed for the purpose of recognizing and
preserving features that exhibit a high degree of predictiveness [45]. The primary
aim of the feature extension technique employed in this chapter is to produce articial features alongside the existing features, thereby enabling the DNN model to
conduct its inherent feature selection on the conceivably enhanced dataset.
This concept was adequately outlined by Mulenga et al. [20], who proposed
feature selection based on conventional normalization methods. The work in this
chapter is slightly different in the sense that, in addition to the sqrt-sum method, it
proposes the use of customized normalization methods for feature extension.
Although the feature extension method has a slightly lower performance than the
sqrt-sum method, it has also been observed that the former outperforms the conventional normalization methods. This shows that feature extension based on customized normalization methods has great potential to improve DNN-based classication
performance of microbiome data. The use of customized normalization methods
could be useful for dynamic implementation of feature extension where normalization methods can be constructed on the y and applied to generate additional features in a dataset. Customized normalization method could be a better alternative to
conventional normalization methods, which are limited by the underlying properties
of individual datasets [19].
M. Mulenga et al.
5 Conclusions
In this chapter, we propose two methods to improve the classication of colorectal
cancer (CRC) using gut microbiome data from stool samples. The rst method,
called sqrt-sum, involves a data normalization technique that adds the standard
deviation of the raw dataset to nonzero items of the same dataset and computes the
square root of the new data points. This helps reduce outliers and dominant features.
The second method, feature extension, generates additional features using the sqrt-
sum method and other customized normalization techniques. It transforms scalar
data points into vectors to enhance feature relevance across the dataset, which is
then subjected to feature selection performed by a DNN model.
Our experimental results show that the sqrt-sum method outperforms baseline
methods in terms of AUC, sensitivity, and specicity. It demonstrates the effectiveness of the sqrt-sum method in improving CRC classication based on gut microbiome data. The feature extension method achieves the second-best performance and
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221
exhibits stability. Furthermore, our ndings highlight the robustness of aggregated
normalization methods in handling anomalies commonly found in datasets. Future
research should explore how these methods perform across different machine learning algorithms and investigate their integration with other feature selection methods, such as lter and ensemble-based approaches. This work contributes to
advancing CRC classication using gut microbiome data by introducing novel
methods and providing evidence of their efcacy. It offers valuable insights for
researchers and practitioners in the eld and serves as a foundation for further
investigations.
Further research is warranted to explore the variations in the effects of aggregate
normalization methods across diverse machine learning algorithms. Understanding
how these methods perform in different ML frameworks would provide valuable
insights for optimizing their application in various contexts. Additionally, an intriguing avenue for future investigation lies in leveraging the feature extension technique
within the context of other feature selection methods, including lter and ensemblebased approaches. Such exploration could yield valuable synergies and potentially
enhance the overall performance and interpretability of CRC classication models
using gut microbiome data.
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Explainable AI (XAI)-Based
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Robot- Assisted Surgical Classication
Procedure
RamSubbaReddySomula, NarsimhuluPallati, MadhuriThimmapuram,
andShobaRaniSalvadi
Abstract
Background of the Work
Many robot-assisted surgical (RAS) procedures may be arranged in a tree structure. RAS can break down the superstate representing every given surgical job into
its constituent states. An important rst step toward several automated surgeonassisting features is the assessment of these discrete states at various time granularities during RAS.
Motivation for the Work
We provide a deep convolutional neural network (CNN) method called Decay,
Transfer, and Compose (DTC) that can estimate both the present super- and negrained states simultaneously. DTC can handle anomalies in the dataset by exploring its class limits by means of a class decay process. Statistics from the da Vinci®Xi
surgical scheme’s endoscope, robotic arms, and system events are all included
into DTC.
R. S. R. Somula
Sunchon National University, Suncheon, South Jeolla, South Korea
N. Pallati (*)
Department of Computer Engineering and Technology, Chaitanya Bharathi Institute of
Technology, Hyderabad, India
M. Thimmapuram
Department of Information Technology, Chaitanya Bharathi Institute of Technology,
Hyderabad, Telangana, India
S. R. Salvadi
Department of AI & DS, Chaitanya Bharathi Institute of Technology,
Hyderabad, Telangana, India
Ltd. 2024
R. Aluvalu et al. (eds.), Explainable AI in Health Informatics, Computational
Intelligence Methods and Applications,
https://doi.org/10.1007/978-981-97-3705-5_11
225© The Author(s), under exclusive license to Springer Nature Singapore Pte
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Contributions in this Chapter
When tested on the HERNIA dataset, which was collected during actual robotic
inguinal hernia repair surgeries, DTC was found to provide reliable estimations of
both the ne-grained public of the procedure. We demonstrate that DTC’s hierarchical structure enhances the state-of-the-art state approximation throughout the full
HERNIA-20 RAS operation. We also evaluate the relative importance of different
kinds of input data and the architecture of DTC in achieving high precision in estimating the status of the operating room.
Keywords Convolutional neural network · Anomalies dataset · HERNIA-20 ·
Robot-assisted surgical process · da Vinci®Xi · Anomalies
R. S. R. Somula et al.
1 Introduction
To treat a variety of illnesses, doctors have begun using robot-assisted surgery
(RAS) [1]. Patients who had RAS procedures reported fewer complications, less
blood loss, and faster recoveries compared to those who underwent open surgery
[2]. Robotic surgical systems, such as da Vinci, are easier to use and safer than laparoscopy because they reduce the need for skilled human intervention. Surgical
robots can also deliver time-stamped data, robot kinematics. There is a wide variety
of applications for the comprehensive and complete description of a surgical operation offered by these sources [3–5]. Emerging research topics on the use of articial
intelligence (AI) in RAS include, but are not limited to, autonomy [6, 7], evaluation
of surgeon ability [8, 9], workow analysis, and so on. Robotic surgical systems
increasingly employ AI for passive virtual xtures [9], advisory information presentation, and automated surgical activity [10]. Accurate understanding of the current
stage is essential for these AI systems to be benecial during surgery. This sensitivity to time should reach into the microsecond range, letting the surgeon attend to the
tiniest of details. Explainable AI, often abbreviated as XAI, refers to the development and deployment of AI systems and models that can provide human- interpretable
explanations for their decisions and actions. In other words, XAI aims to make AI
algorithms transparent and understandable to humans, allowing users to comprehend how and why an AI system arrived at a particular decision or
recommendation.
There is a high degree of consistency in the surgical duties performed during a
certain type of treatment, such as a hernia repair, when using a robotic surgical system [11]. Numerous surgical procedures, such knot-tying and running suture, may
be broken down into repeatable, ne-grained surgical conditions. The fundamental
components of a surgical task may be broken down into these ne-grained conditions, which include the doctor’s activities and conservational observations. In this
chapter, we model a RAS operation as a medical procedure, similar to the signicant modeling approach known as surgical oncology [12]. The hierarchical fuzzy
sets model (HFSM) states that in real-world scenarios, objects often have multiple
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levels of membership to different fuzzy sets, which cannot be effectively represented by a single fuzzy set [13]. In order to more accurately capture the temporal
developments of operations, HFSM models depict the current status of the procedure at each time step using various degrees of temporal granularity. Estimating
surgical (super) states has several uses. Evaluation of surgical competence, coordination of operating-room workow, and retrospective analysis are all facilitated by
the ability to automatically identify the present surgical job [14]. Automating a surgical procedure relies on being able to accurately predict the state of a procedure at
a ne-grained level. In order to successfully implement AI applications in RAS,
simultaneous state estimation at varying temporal granularities is essential.
227
1.1 Background ofSurgical State Estimation
Estimating a state that lasts a few seconds [15], such as moving a needle over tissue,
and estimating a surgical job that lasts notes [16, 17], such as dissecting a hernia
sac, are two examples of the hierarchical surgical state estimation method. These
activities each have their own unique difculties. Surgical states have a ne texture,
yet they are hard to categorize because of their brief duration and the fact that they
change on their own. However, as superstates can last anywhere from 1 min to
10–30min, capturing long term is essential for estimating the surgical job. Data
from a real-world RAS is expensive to collect and very variable. Even for similar
procedures, the viewing angles, backdrops of different patients might vary widely.
Furthermore, surgical procedures might vary from case to case and physician to
surgeon. Surgery state estimate approaches that rely on data face difculties due to
the small size and great variability of real-world RAS datasets.
There have been prior efforts to estimate both super- and ne-grained surgical
states. Many different types of data collected by surgical robots have been employed
by existing ne-grained surgical state estimate algorithms to infer the actual surgical state. Hidden Markov models, temporal clustering [18], and elds have all been
employed by researchers to predict the kinematics of the surgical robot and simulate
state transitions. It has also been proposed that between adjacent items in time
recorded as long short-term memory (LSTM) [19]. Fine-grained surgical state estimations benet from using several data sources such as endoscopic vision [20]. The
use of several input sources in data-driven state estimate approaches allows for the
extraction of a more complete representation of surgical states, leading to greater
estimation accuracy, especially in realistic RAS scenarios. Endoscopic video data
has been heavily utilized in previous segmentation work for surgical phases and
tasks. In order to train a CNN-LSTM, Twinanda etal. [21] employed laparoscopic
surgery endoscopic video annotations. Robot-assisted surgical procedures involve
the use of robotic systems to assist surgeons in performing various types of surgical
interventions. These robotic systems are designed to enhance surgical precision,
dexterity, and control, ultimately improving patient outcomes. XAI is an approach
in articial intelligence that aims to provide transparency and understanding of the
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decision-making process of AI models. When applied to a robot-assisted surgical
procedure, XAI can help enhance the trust and acceptance of the system by providing explanations for the actions and decisions made by the AI-powered robot. Here
is an overview of how XAI can be integrated into a robot-assisted surgical procedure. Evaluate the effectiveness of the XAI techniques in providing meaningful
explanations and supporting decision-making in the robot-assisted surgical procedure. Gather feedback from surgeons or healthcare professionals to assess the utility
and usability of the system. By incorporating XAI techniques into a robot-assisted
surgical procedure, it becomes possible to provide interpretable explanations for the
actions and decisions made by the AI model. This transparency and understanding
can contribute to improved trust, acceptance, and collaboration between the AI system and the surgical team, ultimately enhancing patient outcomes and safety.
R. S. R. Somula et al.
1.2 Contribution
To enhance the effectiveness of pretrained models in the database identication of
surgical state estimation scenarios, this research proposes a CNN on class decomposition, which is known as the Decay, Transfer, and Compose (DTC) model. The
pretrained models can do this by including a class decomposition layer. Each class
in the picture dataset is broken down into numerous subclasses with the goal of
assigning novel tags to the novel collection, where each subsection is considered as
a separate class.
The rest of the chapter is organized as follows: Sect. 11.2 stretches the literature
review, Sect. 11.3 explains the DTC model, Sect. 11.4 illustrates the validation analysis of the proposed DTC model with existing approaches, and Sect. 11.5 provides
a conclusion and future directions.
2 Related Works
Mahmood etal. [22] proposed combining the strengths of residual networks and
dense constructions to provide a surgical equipment segmentation method that is
both accurate and robust. Our proposed method was tested on the dataset and the
abdominal dataset. Associated to state-of-the-art approaches, the consequences of
the experiments prove that the projected approach is far more effective.
In order to routinely recognize and categorize suturing movements for needle
driving attempts, Luongo etal. [23] use deep learning and computer vision. A total
of 2395 movies were used to create datasets for identication, and a total of 511
lms were used to create datasets for classication, each of which was used to train
a computer vision model to predict one of two classes. The networks were trained
using the optical ow information and the raw red, green, and blue pixels from each
frame. Each using an 80/20 split. All models in this study were able to reliably
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229
predict, with probabilities much above chance, both the presence of a gesture and
the kind of gesture (for classication). Our results demonstrate that computer vision
is able to recognize subtle cues that serve to both identify the suturing procedure
and distinguish it from other types of suturing.
Izzetoglu etal. [24] used a sensor called spectroscopy to investigate the hemodynamic vicissitudes produced in the prefrontal cortex area of the brain during the
skill improvement of resident surgeons (fNIRS). Twenty-four surgical residents
from diverse training programs used a RAS simulator over the course of two training sessions (blocks) to complete sponge suturing tasks of varying degrees of difculty. The data shows that the prefrontal cortex’s oxygenation changed signicantly
less during the second training session associated to the rst.
Qin et al. [25] introduced a deep learning-based approach to surgical state
approximation, termed the hierarchical states with deep, which can provide estimates for both the current super- and ne-grained surgical states and takes information such as endoscopic images, robot motion, and robot. After putting HESS-DNN
through its paces on the HERNIA-20 dataset, which was gathered during real-world
robotic inguinal hernia repair surgeries, it is demonstrated that it can accurately
predict both states. We show that HESS-hierarchical DNN structure signicantly
improves state-of-the-art state approximation by applying it to the HERNIA-20
RAS approach. In addition, we explore the role of HESS-DNN architecture and different types of input data in obtaining high accuracy in surgical (super)state
prediction.
Shaei etal. [26] suggested developing an algorithm to objectively measure surgeon distraction during robotic surgery (RAS). Twenty-two medical students had
their EEGs recorded as they performed one of ve crucial SURG-TLX. Part of it
was dedicated to rating how easily one’s mind wanders (scale: 1–20). All sources of
distraction were scored from least distracting (scores of 1–6, subjective label 1) to
most distracting (scores of 7–12, subjective label 2) to most distracting (scores of 13
or more, subjective label 3) (for subjective rating of “3,” scoring range: 13–20).
These thresholds were decided on informally following a discussion among the
participants and experienced surgeons to classify the students’ levels of distraction.
Using SURG-subjective TLX’s assessments of distraction, we were able to evaluate
the efcacy of our method and the proposed categorization scheme. Pearson’s correlation was also used to examine the relationship between the simulator’s measured performance and the participants’ reported levels of distraction (assessment
scores). The projected end-to-end perfect ranked levels of distraction as low (95%
accurate), moderate (89% accurate), and high (95% accurate), respectively.
Fan etal. [27] compared the physiology of open surgery to that of (1) nonsurgical
work and (2) the two surgeon responsibilities in robotic surgery [27]. The physical
demands of 22 surgeons were evaluated using electromyography of the trapezius
muscles and inertial measurement units of the position and crusade of the head,
upper arms, and torso during the course of surgical workdays. In this study, the
physical demands placed on surgeons during open surgery were compared to those
of doctors performing other types of procedures—RAS for the differential analysis.
Open surgery often consumed more than half of a surgeon’s working day. When
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