On Fri, Feb 23, 2018 at 11:51 Sandor Ferenczy <sandorferenczy@xxxxxxxxx>
wrote:
No. we meant screening 15,000 caucasians could yield 300.
The differing numbers (ie 5/6 in a million vs. 324 in 15,000) are
incidence (diagnosed) vs. prevalence (actual occurrence)
General population screening is how we attempt bridge the gap between
occurrence & diagnosis.
In a multi-ethnic population, nevi occur in 5% of 2x 45 degree FOV fundus
images (relatively low coverage of the retina)
Diabetic studies have found 12% occurrence of nevi in wide-field imaging.
18% of detected nevi have been reported to show growth, and these with
growth then represent 3x higher metastatic risk.
in an otherwise unscreened, asymptomatic population:
12% of 15,000 is 1800 potentially with a melanocyctic tumor
18% of 1800 is 324 potentially with a growing melanocytic tumor & 3x
higher risk of metastatic disease.
Qiu M, Shields CL. Choroidal nevus in the United States population. Racial
disparities and associated factors in the National Health and Nutrition
Examination Survey. Ophthalmology 2015;122:2071-2083.
Silva PS, Cavallerano JD, Haddad NMN, et. al. Comparison of nondiabetic
retinal findings identified with nonmydriatic fundus photography vs
ultrawide field imaging in an ocular telehealth program. JAMA Ophthalmol
2016;134(3):330-334.
Shields CL, Shields JA, Kiratli H, et. al. Risk factors for growth and
metastasis of small choroidal melanocytic lesions. Ophthalmology
1995;102:1351-1361.
This simply showcases two things:
1) the disparity between natural occurrence of the tumors (whether benign
or malignant) and clinical diagnosis (ie we need better population
screening)
2) the shortcomings of smaller FOV screening @ eye doctors offices. (< 50
degree FOV vs. wide field)
Caveat - we did this statistical analysis for grant applications for
general population screening. It is a statistical possibility, and in our
minds a reason to increased screening, but the hard general population data
is not known yet.
-sandor
On Feb 22, 2018, at 10:39 AM, Egnatz, Thomas <tegnatz@xxxxxxxxx> wrote:melanomas?
Sandor,
Did you perhaps mean screening 15,000,000 Caucasians would yield 300
melanomas.[1,2,3] In the United States, the incidence of uveal melanoma is
But that should only be 75 melanomas.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399142/
Uveal melanoma represents 79-81% of ocular melanomas and 3-5% of all
5/million population.
behalf of Sandor Ferenczy <sandorferenczy@xxxxxxxxx>
Tom
From: optimal-bounce@xxxxxxxxxxxxx <optimal-bounce@xxxxxxxxxxxxx> on
Sent: Wednesday, February 21, 2018 9:38 AMNeural Nets
To: optimal@xxxxxxxxxxxxx
Subject: [optimal] Re: [**External**] Google and Ophthalmic Imaging
blood pressure system @ the local pharmacy.
I more picture a self-guided imaging system next to the self-guided
images with machine learning, abnormal findings whether vascular, nerve,
Imagine the power of screening tens of thousands of wide field, color
retinal, tumor can be forwarded directly to the existing reading centers @
the world's best hospitals & patient be told to follow up with a local
ophthalmologist.
15,000 Caucasians in the US would yield 300 growing melanocytic tumors in
In statistical work we have done, we estimate that screening of only
the uvea.
...imagine...we just need to train them what to look for.
No human could do intra ocluar screening as efficiently as a computer.
bioartevolution@xxxxxxxxx> wrote:
-sandor
On Wed, Feb 21, 2018 at 9:18 AM, joey hatfield <
Imagine, program guided self assessment fundus imaging on a phone thatcan send a report to the physician. Evaluation of the posterior pole for
this application, diabetic changes, etc...AI is very real and being looked
at heavily by most major companies.
wrote:
Joey Hatfield
Area Sales Consultant
Heidelberg Engineering
Mobile: 501-515-0697
email: jhatfield@xxxxxxxxxxxxxxxxxxxxxxxxx
10 Forge Parkway l Franklin, MA l 02038
Tel:
Fax:
www.heidelbergengineering.com
www.spectralis.info
On Feb 21, 2018, at 8:06 AM, Sandor Ferenczy <sandorferenczy@xxxxxxxxx>
on training rather than explicit programming) is real, has been around
Neither fake news or bad science.
Machine learning (a computer's progressive improvement at a task based
since the '50s, and is more and more being applied to medicine.
two paragraphs:
The most important part of this general public article are the second
blood tests, according to a study of the finding published in the journal
The algorithms didn’t outperform existing medical approaches such as
Nature Biomedical Engineering. The work needs to be validated and repeated
on more people before it gains broader acceptance, several outside
physicians said.
providing a tool that people could one day use to quickly and easily screen
But the new approach could build on doctors’ current abilities by
themselves for health risks that can contribute to heart disease, the
leading cause of death worldwide.
cpmceyelab@xxxxxxxxxxxxxxxx> wrote:
-sandor
On Tue, Feb 20, 2018 at 9:34 PM, CPMC Ophthalmic Diagnostic Center <
strokeI am unclear as to how these poor Topcon images would help screen for
signs that would send a patient for a Carotid angiogram.Unless they are referring to H. Plaque, Occlusions, etc and the usual
Neural NetsFake news or bad science?
Denice Barsness, CRA, COMT, CDOS, FOPS
CPMC Dept of Ophthalmology/ The Eye Institute
Ophthalmic Diagnostic Services
711 Van Ness Avenue Suite 250
San Francisco CA 94109
415-600-5781
FAX 415-558-7011
From: Barsness, Denice
Sent: Tuesday, February 20, 2018 3:30 PM
To: CPMC Ophthalmic Diagnostic Center <cpmceyelab@xxxxxxxxxxxxxxxx>
Subject: FW: [**External**] [optimal] Google and Ophthalmic Imaging
(US & Canada)
From: optimal-bounce@freelists.orgOn Behalf Ofjmc eye photo
Sent: Tuesday, February 20, 2018 3:29:24 PM (UTC-08:00) Pacific Time
NetsTo: optimal@xxxxxxxxxxxxx
Subject: [**External**] [optimal] Google and Ophthalmic Imaging Neural
system!
WARNING: This email originated outside of the Sutter Health email
ID or Password.DO NOT CLICK links if the sender is unknown and never provide your User
the notch says otherwise.
Interesting article at link below. Article says images are scans, but
https://www.washingtonpost.com/news/the-switch/wp/2018/02/19/google-used-artificial-intelligence-to-predict-heart-attacks-with-the-human-eye/?utm_term=.27acdee9d2c2
Anyone out there participating is such studies?
Richard Press posted about a similar project re diabetes last march.
ALSO — Is Google a sustaining member of OPS?
john michael coppinger
jmc eye photo
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