SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial
Life Vol. 10(4); March (2024), pp. 318–322
ISSN (Online): 2352-6386; ISSN (Print): 2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Characteristics of The Reciprocal Movement of Radiographers’
Gaze through A Comparison of Entry-Level and Experienced
Radiographer
Yuka Naito1, Jun Nakamura2, Yoshinobu Ishiwata3
Graduate School of Economics, Chuo University, Higashinakano Hachioji-shi, 192-0393, Japan
Faculty of Global Management, Chuo University, Higashinakano Hachioji-shi, 192-0393, Japan
3
Graduate School of Medicine, Yokohama City University, 3-9, Fukura, Yokohama Shi Kanazawa Ku, 236-0004, Japan
1
2

ARTICLE INFO
Article History
Received 25 December, 2023
Accepted 15 April 2024
Keywords
Eye-tracking
Angles of eye trajectory
Artificial intelligence (AI)ABSTRACTWith the development of AI, AI may substitute for physicians in radiological interpretations in
the future. The purpose of this paper is to identify factors that could form the foundation foralgorithms enabling AI to explore pathological findings. This study focuses on the "angles of gaze
trajectory" and "average speeds of gaze movement," and conducts a comparative analysis between
entry-level and experienced radiographers. Firstly, based on the results of angles of gazemovement, experienced radiographers exhibited frequent movements around the 180-degree markcompared to entry-level radiographer. Both entry-level and experienced radiographers commonly
displayed frequent movements around 0 degrees and near 350 degrees. Secondly, according to
the results of average speeds of gaze movement, entry-level radiographers were found to havefaster movement speeds compared to experienced radiographers.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

gaze patterns as a contributing factor, underscoring thenecessity to uncover and comprehend these aspects toenhance the AI learning process in image interpretation.

1. Introduction
Machine learning and deep learning advancements,
coupled with high-performance GPUs, have led to asignificant breakthrough in the field of artificial
intelligence [1]. Particularly in image recognition, AI isconsidered to surpass human capabilities [2]. In thehealthcare sector, there is a clear demonstration of AI's
image recognition capabilities, particularly in theinterpretation of CT scans. Given the current shortage ofradiologists, there is growing anticipation for AI to reacha level of proficiency in image interpretation that could
serve as a viable substitute for human physicians. To
achieve this capability, it is crucial to identify various
factors contributing to the training of AI in image
interpretation. One such factor is the analysis ofphysicians' eye movements during image interpretation.
This paper aims to explore the importance of physicians'

2. Previous Research
Numerous studies have explored gaze measurementsof physicians during the interpretation of radiographic
images, a certain cue is presented on the screen, and ananalysis is conducted on how participants can detect thiscue in terms of gaze trajectory and movement velocity [3].
Studies comparing the gaze patterns of novices andveterans are also prevalent. For instance, an analysis ofgaze fixation points during chest X-ray image
interpretation shows that experts promptly identify
lesions and repeatedly confirm their presence while alsoscanning other areas, whereas novices tend to fixate on
structurally complex regions [4]. In research on

Corresponding author E-mail: a19.3gem@g.chuo-u.ac.jp, jyulis.77f@g.chuo-u.ac.jp, ishi_y@yokohama-cu.ac.jp318

overlooking and missing findings during medical image
interpretation, it has been demonstrated that experts
thoroughly observe photographs, strongly focusing on
the accurate location of anomalies, while novices exhibit
more inconsistent observation patterns, with their gaze
often diverted to complex positions where organs overlap[5]. This line of research on tacit knowledge and expertisein skill-demanding scenarios has unveiled that veteransand novices differ in terms of "where to look,"
influencing their subsequent performance [6]. As apreliminary step, it is necessary to extract andquantitatively analyze the gaze patterns of experts before
linguistically formulating such characteristics. Therefore,
the purpose of this paper is to conduct a comparative
analysis between entry-level and experienced
radiographers regarding the angles of gaze trajectories,
which could serve as the foundation for algorithms
exploring findings in artificial intelligence (AI).
Furthermore,theobjectiveistoproposerecommendations for AI to learn from the disparities ingaze trajectories between entry-level and experienced
radiographers, ultimately enabling more accurate andeffective image diagnostics in the future.

images is consistent with the characteristics of everyday
human gaze as elucidated in previous research [9]. The
characteristics of human eye movement elucidated in thisprevious research are detailed in the subsequentdiscussion section. The employed eye-tracking softwarein this study automatically aggregates parameters such as
fixation points, fixation counts, fixation duration, andcoordinates of fixation points, subsequently generating
this information as output data. The trajectories
connecting successive fixation points (hereinafterreferred to as gaze trajectories) are visually indicated by
red lines, enabling direct observation.
The angles of gaze movement were defined based on
the eye-tracking data obtained using Tobii Pro Lab andanalyzed using a custom-developed program. In thiscontext, angles of gaze movement refer to the directionchanges of gaze trajectories as it progresses acrossdifferent fixation points. The concept is illustrated in theFig. 1, where black circles represent fixation points(pixel), and connecting lines depict the gaze trajectory
between these points. When the coordinate shifts from(971, 810) to (988, 867), and subsequently to (974, 816),
the angle of gaze movement is denoted by Angle 1 in Fig.
1. The directional movement is measured clockwise.
Similarly, when moving from (988, 867) to (974, 816)
and further to (1013, 837), the angle of gaze movement isrepresented as Angle 2 in Fig. 1.

3. Experiment
The gaze measurement experiment was carried out asfollows.
 Participants: One experienced radiographer (withover 10 years of experience) and one entry-level
radiographer (with 1~3 years of experience).
 Data:15 cases with abnormal findings, 15 cases
without abnormal findings
 Experimental method: Tobii Pro Lab eye-tracking
software was employed. The camera was fixed.
Participants were presented with CT images on acomputer screen and performed interpretation of
CT images by scrolling through the images using amouse, following their usual reading methods.

Fig.1 Angles of gaze movement
In addition to the angle of gaze movements, theaverage speeds of gaze movement are calculated. The
average speeds of gaze movement are calculated by
dividing the distance of gaze movement measured by thedevice by the elapsed measurement time. The average
speeds of gaze movement were calculated for both entrylevel and experienced radiographers, separately for caseswith and without abnormal findings.

4. Analysis Methods
Duchowski provides guidelines regarding datacollection and interpretation in the context of eye tracking.
The book introduces applications and instances ofsuccess in fields such as medicine, psychology, andhuman factors engineering, underscoring theeffectiveness of employing eye tracking in the medical
domain [7]. Furthermore, Holmqvist et al. offer an indepth exposition of data analysis methods in eye tracking
research. Of particular note, fixation points serve as afrequently employed metric [8]. However, the analyzed
metric in this paper is the angle of the gaze movement as
it sequentially moves from one fixation point to the next
in succession. (Henceforth, this shall be referred to theangle of eye movement.) The reason for this is to confirm
whether the direction of eye movement, namely the
trajectory of the gaze, when radiologists interpret CT

5. Results
The results of gaze angles are depicted in Fig. 2, Fig.
3, Fig. 4, and Fig. 5. Fig. 2 and Fig. 3 present the gazeangle results of experienced individuals. Fig. 2
corresponds to cases with abnormal findings, while Fig.
3 represents cases without abnormal findings. Fig. 4 andFig. 5 illustrate the gaze angle results of entry-level
radiographer. Fig. 4 pertains to cases with abnormal319

findings, whereas Fig. 5 depicts cases without abnormal
findings.

A comparison between entry-level and experienced
radiographers revealed notable differences in angles ofgaze movement. In the graphs, the numbers displayed on
the outer circumference represent the angles, while thenumbers within the circumference, namely the radii,
indicate the frequency of the corresponding angles.
Skilled practitioners exhibited a higher frequency of gazemovement angles around 180 degrees within the 360degree spectrum. This is demonstrated in Fig. 2 and Fig.
3, representing the results with and without abnormal
findings, respectively. In Fig. 2, angles such as 10 degrees,
350 degrees, 190 degrees, and 180 degrees were observedwith higher frequencies. Similarly, in Fig. 3, angles like10 degrees, 180 degrees, and 350 degrees were more
frequent. When examining the results for entry-level
radiographer, as shown in Fig. 4 and Fig. 5 forinterpretations with and without abnormal findings,
respectively, angles such as 350 degrees and 10 degrees
were more observed in their gaze movement. Comparing
the results presented in Fig. 2 and Fig 4, or comparing theresults presented in Fig. 3 and Fig. 5, it is evident thatexperienced radiographer frequently exhibit gazemovement angles near 180 degrees, a pattern not as
pronounced in novice practitioners. This suggests thatexperienced radiographer tends to pause and carefully
observe between fixation points when shifting their gaze,
while entry-level radiographer appear to transition theirgaze more promptly from one point to another.

Fig.2 Angles of gaze movement in experienced
radiographer (with abnormal findings)

Fig.3 Angles of gaze movement in experienced
radiographer (without abnormal findings)

6. Discussion
The paper highlighted differences in gaze movement
between entry-level and experienced radiographersduring interpretation. The analysis of angles of gazemovement, a hypothesis was formulated. The hypothesis
suggests that skilled practitioners pause for carefulobservation between fixation points when shifting theirgaze, whereas novices shift their gaze more promptly
from one point to another, lacking this intermediate pause.
While not initially planned, based on the results of angles
of gaze movement, average speeds of gaze movement
were calculated using accumulated distance and time.
The following Table 1 represents those results.

Fig.4 Angles of gaze movement in entry-level
radiographer (with abnormal findings)

Table 1: Average speeds of gaze movement
AverageWith
Without
speeds(pixel/sec)
abnormalabnormalfindings
findings
Experienced
533.24218.8289
radiographer
Entry-level
13966.75
2893.048
radiographers
It is evident that entry-level radiographer has a fasteraverage speeds of gaze movement compared toexperienced radiographer. The accumulated distance ofentry-level radiographer was longer than experienced

Fig.5 Angles of gaze movement in entry-level
radiographer (without abnormal findings)
320radiographer. The distinction in accumulated distanceand average speed suggests that entry-level radiographer
tends to survey a wider area rapidly, whereas maintains
experienced radiographer a deliberate focus on specificregions. Calculating average speeds of gaze movement
further supported the hypothesis stated above.
Considering that entry-level radiographer possesses
less experience than skilled practitioners, they might findit challenging to predict the location of anomalies. This
could potentially explain why entry-level radiographers
tend to survey the entire CT images thoroughly andquickly. In contrast, experienced radiographer can infer
the approximate location of anomalies, allowing them toconcentrate on specific areas. Interviews withexperienced practitioners aligned with this explanation.
Inexperienced individuals may have attempted to observea broad area as much as possible. As a result, the gaze ofentry-level radiographer may have exhibited movements
along the Z-axis (the head-foot direction in CT images),
while simultaneously surveying the XY plane
comprehensively. Conversely, experienced radiographer
tends to direct their gaze along the Z-axis (head-footdirection). Therefore, in a series of similar CT images,
should an anomaly be detected, it could be recognized asa flicker. According to the previous research [9], thecharacteristics of human gaze encompass distinct roles
for central vision and peripheral vision, where graspingstimuli occurs through peripheral vision andsubsequently confirming details via central vision
constitutes a fundamental process. Experienced
radiographers shift their gaze along the Z-axis (head-footdirection) with peripheral vision, and subsequently
enhance the resolution with their central vision whenanomalies are detected. This phenomenon aligns with thedescription provided by adept physicians as "recognizing
anomalies as flickers."
Based on physiological factors in previous research[10], the reciprocal motion of gaze being predominantly
horizontal and vertical rather than up and down has beenfrequently observed. In this study, consistent with Fig. 2
through Fig. 5, reciprocal movements are frequently
observed, and upon examining raw gaze data acquired
from the device, it was found that horizontal reciprocal
gaze movements are more prevalent than vertical ones.
Additionally, the gaze patterns of experienced
radiographer
revealedrepeatedback-and-forth
movements along lines near 180 and 360 degrees. Similar
repetitive movements along horizontal and vertical lines
have been observed in previous studies [9]. The researchhas been elucidated that repetitive movements alonghorizontal lines are more frequent than those along
vertical lines. In other previous research [11], it hasrevealed that the capability to accurately perceive
information is higher in the horizontal direction of eye
movements compared to the vertical direction. This paper
also found that entry-level radiographer exhibited more
frequent back-and-forth movements along horizontal
lines than vertical ones. This phenomenon can be
attributed to reasons such as the relatively easierexecution of horizontal movements compared to verticalones, as well as the cautious tendency of entry-level
radiographers due to the difficulty in anticipating thelocation of abnormal findings. Consequently, entry-level
radiographers may opt to observe a broader scope of theCT image.
7. Conclusions
The purpose of this paper is to conduct a comparative
analysis between entry-level and experienced
radiographers regarding the angles of gaze trajectories,
which could serve as the foundation for algorithms
exploring findings in artificial intelligence (AI).
Furthermore,theobjectiveistoproposerecommendations for AI to learn from the disparities ingaze trajectories between entry-level and experienced
radiographers, ultimately enabling more accurate andeffective image diagnostics in the future. This paper
specifically analyzed the angles of gaze movement andspeed of entry-level and experienced radiographers.
However, it is important to note that this research had
limitations in terms of a limited number of participants
and sample size. Future studies should involve a larger
sample size to further investigate the elements ofalgorithms during radiological interpretation.
8. Acknowledgements
The development of a program for analyzing gazemovement angles in this study was made possible
through the support of Focus Systems Corporation. The
authors express their deep gratitude for their assistance.
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Authors Introduction
Ms. Yuka Naito

She is a 1st year graduate student at thefaculty of Economics, Chuo University,
Japan. The areas of his research interest
are Experimental Economics, Behavioral
Economics, Cognitive Psychology, andArtificial Intelligence.

Dr. Jun Nakamura
He is a professor at the Faculty of Global
Management, Chuo University, Japan.
Previously he was working as a professor
at the Graduate School of Engineering
Management, Shibaura Institute ofTechnology. His main research areas are
Cognitive Science and Humanities.
Dr. Yoshinobu Ishiwata
He is an assistant professor at the
department of radiology, Yokohama City
University School of Medicine. The areasof his research interest are Diagnostic
Radiology, Nuclear Medicine, andArtificial Intelligence.

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