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Automated visual evaluation (AVE) is a promising
technology that uses a machine learning (ML)
classifier to predict the likelihood of pathology in a
cervical image. AVE is accurate, fast, and
inexpensive, and thus it has tremendous potential
for utilization in low resource settings (LRS), where
cytology, and at times HPV testing, are not readily
available.
Introduction
An existing AVE classifier was integrated into
the EVA System as web service. The
classifier was deployed on the cloud and is
called by the provider from the patient
records on the EVA image portal.
Methods
Discussion
Results
Performance of automated visual evaluation as a triage test for HPV+ patients from a
screening camp in rural China TAP TO RETURN
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Source: Wikipedia
Test inadequacy rates
• Colpo impression: 17/147 = 11.6%
Defined as “Unable to make colposcopic
impression”
• AVE: 2/147 = 1.3%
Defined as “patient case did not sync to
portal in reasonable time to provide the
patient an answer”
• First use of AI for cervical cancer detection
at PoC in LRS
• Low failure rate for AVE, in comparison to
colposcopic impression
• 3G connectivity sufficient for running classifier
on EVA portal
▶ Further testing needed for areas with lower
connectivity
• Classifier performance comparable to those of
triage technologies reported in the literature
from high resource clinic.
• AVE performance was poorer on these images
than in global validation set. These differences
are explained by model generality, observed
between External test set vs. Holdout test set
The EVA System was
used for imaging HPV+
patients, as part of the
care provided in a
screening camp in Inner
Mongolia, China.
Suspect regions on the
cervix were biopsied
and analyzed in United
Family Healthcare
Hospitals in Beijing. AVE
scores were compared
to histopathology
results. Positives were
defined by CIN 2+
histopathology.In order to be deployed in LRS, AVE needs to be
integrated into an imaging device with computing
power that can operate in such a setting. One
device that meets this criteria is the Enhanced
Visual Assessment (EVA) System (MobileODT), a
cloud-connected mobile colposcope used in >40
countries globally.
The goal of this study is to determine whether a
preliminary version of AVE can be used at the point
of care (PoC) in LRS, and how it integrates into the
clinical workflow. Here, AVE was used to assess
patients in a screening camp in Inner Mongolia,
China.
AT Goldstein1, S Bedell1, R Lipson2, CM Sebag3, L Lobel3, D Levitz3
1 Center for Vulvovaginal Disorders, Washington, DC, USA; 2 United Family Healthcare Hospitals, Beijing, China; 3 MobileODT Ltd., Tel Aviv, Israel
A Receiver Operating Characteristic curve
was calculated on AVE scores, showing AVE
to be more sensitive than specific on HPV+
patients from the screening camp.
A secondary analysis also assessed test
reliability at the PoC. Test inadequacy rates
were measured against colposcopic impression.
Colposcopic impression vs. AVE
AVE+ AVE-
Colpo + 112 11
Colpo- 5 0
Running AVE at the point of Care
522-992 women/day self-swabbed for high-risk HPV (hrHPV+). Two Ampfire HPV PCR systems
were run simultaneously to test the specimens. All hrHPV+ patients had digital colposcopy (DC)
performed on the same day with the EVA system. Digital images were obtained, and all
suspected lesions were biopsied. Suspected CIN1 and CIN2 lesions were treated with thermo-
coagulation, and suspected CIN3 lesions were treated with LEEP.
Screening camp information CONCLUSIONS
The tested implementation of AVE – uploading
images to the patient file and running the
classifier immediately – proved feasible for use
in low resource settings.
The inadequacy rate of AVE – cases that did not
sync – was very low (1.3%), and smaller than the
rate at which colposcopic impression was not
feasible.
In comparison to data from other studies on
triage of HPV+ patients, the performance of AVE
compared favorably to that of existing and
emerging triage methods including cytology, dual
p16/Ki67 stain, and DNA methylation.
NEXT STEPS
External confirmation of biopsy + Adjudication
on discordant cases (in progress)
Reassess accuracy and ROC given “new ground
truth” labels
EC2 instance
(AWS)
1
2
3
4 5
6
Colposcope
Portal
• Integrated button on portal
• Initiates EC2 instance on Amazon Web
Services’ servers
▶ Input: single image
▶ Output: prediction score
• Total time: ~5-10 min
• Feature piloted in Korea for 1 year
▶ Almost 7000 patients
The EVA image portal provided the secure
connection to the AVE classifier on the cloud. It
allows for sending images to a remote server for
analysis, and returns an answer.
References
Hu et al, J NCI 2019
Demarco et al, IPVC 2018
Wentzensen et al, J AMA Int Med, 2019
AT Goldstein1, S Bedell1, R Lipson2, CM Sebag3, L Lobel3, D Levitz3
Performance of automated visual evaluation as a triage test for HPV+ patients from a
screening camp in rural China
1 Center for Vulvovaginal Disorders, Washington, DC, USA; 2 United Family Healthcare Hospitals, Beijing, China; 3 MobileODT Ltd., Tel Aviv, Israel
The EVA image portal –
a secure location for
saving patient data on
the cloud – is an
integral part of the
EVA System. 3345 women were
screened/treated by 5
physicians in 5 days. 619
women (18.5%) were
hrHPV+. 589 women
underwent same-day
colposcopy. Biopsy: 108
CIN1, 22 CIN2, 9 CIN3.
AVE classifier AVE data analysis
• Trained on manually annotated
images from 1473 patients
• Data came from 17 countries,
with heavy representation from
Kenya and India.
• Based on Faster RCNN
architecture running on Caffe During data analysis, a secondary ML classifier (the qualifying
classifier) was used to filter out bad images prior to further analysis.
The qualifying classifier also cropped the cervix region in the
images, to ensure the prediction is not affected by artifacts.
There were multiple images captured per patient, each with varying
quality. To combine the AVE prediction scores from multiple images
in one patient, a weighted average of the scores was calculated,
using the image quality scores as the weights.
Funding
This work was funded by Gynecologic Cancers
Research Foundation.

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Performance of automated visual evaluation as a triage test for HPV+ patients from a screening camp in rural China

  • 1. Automated visual evaluation (AVE) is a promising technology that uses a machine learning (ML) classifier to predict the likelihood of pathology in a cervical image. AVE is accurate, fast, and inexpensive, and thus it has tremendous potential for utilization in low resource settings (LRS), where cytology, and at times HPV testing, are not readily available. Introduction An existing AVE classifier was integrated into the EVA System as web service. The classifier was deployed on the cloud and is called by the provider from the patient records on the EVA image portal. Methods Discussion Results Performance of automated visual evaluation as a triage test for HPV+ patients from a screening camp in rural China TAP TO RETURN TO KIOSK MENU Source: Wikipedia Test inadequacy rates • Colpo impression: 17/147 = 11.6% Defined as “Unable to make colposcopic impression” • AVE: 2/147 = 1.3% Defined as “patient case did not sync to portal in reasonable time to provide the patient an answer” • First use of AI for cervical cancer detection at PoC in LRS • Low failure rate for AVE, in comparison to colposcopic impression • 3G connectivity sufficient for running classifier on EVA portal ▶ Further testing needed for areas with lower connectivity • Classifier performance comparable to those of triage technologies reported in the literature from high resource clinic. • AVE performance was poorer on these images than in global validation set. These differences are explained by model generality, observed between External test set vs. Holdout test set The EVA System was used for imaging HPV+ patients, as part of the care provided in a screening camp in Inner Mongolia, China. Suspect regions on the cervix were biopsied and analyzed in United Family Healthcare Hospitals in Beijing. AVE scores were compared to histopathology results. Positives were defined by CIN 2+ histopathology.In order to be deployed in LRS, AVE needs to be integrated into an imaging device with computing power that can operate in such a setting. One device that meets this criteria is the Enhanced Visual Assessment (EVA) System (MobileODT), a cloud-connected mobile colposcope used in >40 countries globally. The goal of this study is to determine whether a preliminary version of AVE can be used at the point of care (PoC) in LRS, and how it integrates into the clinical workflow. Here, AVE was used to assess patients in a screening camp in Inner Mongolia, China. AT Goldstein1, S Bedell1, R Lipson2, CM Sebag3, L Lobel3, D Levitz3 1 Center for Vulvovaginal Disorders, Washington, DC, USA; 2 United Family Healthcare Hospitals, Beijing, China; 3 MobileODT Ltd., Tel Aviv, Israel A Receiver Operating Characteristic curve was calculated on AVE scores, showing AVE to be more sensitive than specific on HPV+ patients from the screening camp. A secondary analysis also assessed test reliability at the PoC. Test inadequacy rates were measured against colposcopic impression. Colposcopic impression vs. AVE AVE+ AVE- Colpo + 112 11 Colpo- 5 0
  • 2. Running AVE at the point of Care 522-992 women/day self-swabbed for high-risk HPV (hrHPV+). Two Ampfire HPV PCR systems were run simultaneously to test the specimens. All hrHPV+ patients had digital colposcopy (DC) performed on the same day with the EVA system. Digital images were obtained, and all suspected lesions were biopsied. Suspected CIN1 and CIN2 lesions were treated with thermo- coagulation, and suspected CIN3 lesions were treated with LEEP. Screening camp information CONCLUSIONS The tested implementation of AVE – uploading images to the patient file and running the classifier immediately – proved feasible for use in low resource settings. The inadequacy rate of AVE – cases that did not sync – was very low (1.3%), and smaller than the rate at which colposcopic impression was not feasible. In comparison to data from other studies on triage of HPV+ patients, the performance of AVE compared favorably to that of existing and emerging triage methods including cytology, dual p16/Ki67 stain, and DNA methylation. NEXT STEPS External confirmation of biopsy + Adjudication on discordant cases (in progress) Reassess accuracy and ROC given “new ground truth” labels EC2 instance (AWS) 1 2 3 4 5 6 Colposcope Portal • Integrated button on portal • Initiates EC2 instance on Amazon Web Services’ servers ▶ Input: single image ▶ Output: prediction score • Total time: ~5-10 min • Feature piloted in Korea for 1 year ▶ Almost 7000 patients The EVA image portal provided the secure connection to the AVE classifier on the cloud. It allows for sending images to a remote server for analysis, and returns an answer. References Hu et al, J NCI 2019 Demarco et al, IPVC 2018 Wentzensen et al, J AMA Int Med, 2019 AT Goldstein1, S Bedell1, R Lipson2, CM Sebag3, L Lobel3, D Levitz3 Performance of automated visual evaluation as a triage test for HPV+ patients from a screening camp in rural China 1 Center for Vulvovaginal Disorders, Washington, DC, USA; 2 United Family Healthcare Hospitals, Beijing, China; 3 MobileODT Ltd., Tel Aviv, Israel The EVA image portal – a secure location for saving patient data on the cloud – is an integral part of the EVA System. 3345 women were screened/treated by 5 physicians in 5 days. 619 women (18.5%) were hrHPV+. 589 women underwent same-day colposcopy. Biopsy: 108 CIN1, 22 CIN2, 9 CIN3. AVE classifier AVE data analysis • Trained on manually annotated images from 1473 patients • Data came from 17 countries, with heavy representation from Kenya and India. • Based on Faster RCNN architecture running on Caffe During data analysis, a secondary ML classifier (the qualifying classifier) was used to filter out bad images prior to further analysis. The qualifying classifier also cropped the cervix region in the images, to ensure the prediction is not affected by artifacts. There were multiple images captured per patient, each with varying quality. To combine the AVE prediction scores from multiple images in one patient, a weighted average of the scores was calculated, using the image quality scores as the weights. Funding This work was funded by Gynecologic Cancers Research Foundation.