Osher: During the past few years, we've heard
so much about artificial intelligence,
so it doesn't surprise me
that a video on AI was produced
by the cutting-edge group from India, Doctors
Vasavada, Srivasatava, Puri, Vasavada,
and of course, the renowned Doctor
Abhay Vasavada.
Narrator: Probable diagnosis
for this patient is diabetic macular edema.
You guessed it right? I am artificial intelligence
and I'm working hand in hand with the ophthalmologist.
AI is often perceived as a sci-fi dystopian future,
with man vs machine,
when in fact it's quite the opposite.
I'm here to show you what lies ahead.
Vasavada: Future forward:
artificial intelligence-assisted eyecare.
Artificial intelligence has already entered health care,
and is used for problems such as breast cancer screening,
cardiovascular risk assessment, outcome prediction, planning
of treatment, and managing records.
AI has also made its foray into ophthalmology.
It is indeed time.
We ask ourselves, are we the ophthalmologists ready for AI,
or do we want techies
to define the way we practice in our future?
We therefore decided to take matters in our own hands.
We created two in-house models using AI,
which were then used in our day-to-day clinics.
Retinal OCT image classification using deep-learning models.
A retinal OCT classifier model was created using OCT images
available from an open source.
We trained the model with over 80,000 OCT images
to identify 4 diseases: choroidal neovascularization,
diabetic macular edema, drusen,
and normal retinal configuration.
Here, we use the Resnet 18 architecture.
Resnet 18 is a convolutional neural network
that is trained on more than a million images from
the ImageNet database.
A convolutional neural network is a deep-learning algorithm
that can take in an input image, assign importance
to various aspects in the image,
and be able to differentiate one from the other.
The architecture of a CNN was in fact inspired
by the organization of the visual cortex.
The CNN reduces the image into a form that is easy
to process without losing features
that are critical for a good prediction.
The model was given a set
of 5000 fresh images in order to test it.
To make our model more relatable, we started
with a user interface that looks like a webpage.
When presented with an OCT image, the model runs
behind the scenes and gives us a predictive diagnosis.
When the model was deployed,
its accuracy in making the right diagnosis was close to 98%.
However, in clinics,
we would trust any model only if it is able
to justify why the particular diagnosis was chosen over the
others, and this is the explainability of the model.
Whenever we look at the prediction from an AI model,
a crucial aspect to consider is the confidence
score of the diagnosis.
Look at this example.
Here, the model gives us a diagnosis
of choroidal neovascular membrane.
What's more important? The confidence score
for CNVM is exceptionally high when compared
to other diseases in the model.
Let's look at another example.
Here, the model picked up a drusen correctly,
assigning it a very high confidence score.
As a step further in the explainability,
we use the class activation map to understand
which were the exact areas
and pixels of the OCT image that were chosen by the model
to arrive at the diagnosis.
We can clearly see what region
of interest the model has selected to give the diagnosis,
and it is very similar to what a clinician would choose.
An explainable model instills confidence in the surgeon
regarding its accuracy.
More importantly, if an error is made by the model,
it can be identified
and the model retrained to improve its performance.
This is why it's important
that the ophthalmologist interacts with the model.
An explainable interactive AI model can be trained
to get better and upgrade itself.
Prediction of refractive error using ocular biometry
with artificial intelligence.
The most elementary in our practice is
to determine the refractive error of the eye.
We know that each biometric parameter influences the total
refractive error, and yet currently the only way
to determine the refractive error is
by relying either on retinoscopy or auto refractors.
So, we created a model that offers a completely new outlook
to objectively determine the refractive error within an eye.
Ocular biometric data was captured
for over 5000 normal eyes aged 18 to 45 years.
This was input in regression models created
using XG boost and deep learning.
Let's look at this case here.
The model predicts a refractive error
of minus 3 diopters based on biometry.
When crosschecked by conventional methods, the refraction
of the eye was found to be minus 3.5 diopters.
Also, changing any biometric parameter automatically
changes the refractive error.
Understanding and exploring AI helped us
get previously unknown insights into the contribution
of different biometric parameters to the refractive error.
If successful, this would be a completely novel way
to assess refractive errors.
There are several unexplored avenues
where, if used correctly, AI will open new doors.
The big fear that hounds us all is,
will AI make the manpower redundant?
The clear answer is no.
In fact, incorporating explainable AI has the potential
to take away the repetition in our lives, allowing us
to focus more on problems that only humans can solve.
Imagine how AI can be a boon in those regions
and populations where access to basic healthcare is scarce
and often challenging.
But as with any new technology,
amalgamating AI will also require us
to make changes in our practices.
The prerequisite to robust AI models is meaningful data.
Therefore, we need to create a system that allows
for accurate and easily accessible data.
To sum up, explainable AI is the future.
As clinicians, it is of paramount importance
that we be equal and active partners with the industry
in order to keep AI relevant, contextual,
and ever evolving, so that it becomes a tool
that helps us harness our potentials much better.
Buratto: Hi Bob. Congratulations for the 30 years.
This Video Journal of Cataract Surgery is
very important journal.
You teach that all the students, all the doctors,
all the surgeon all around the world.
Really, your project was a great project,
is a great project.
I really understand what you mean with your videos
because I organized the video cataracts since 45 years
and I always used the videos for teaching
and for demonstrating complication and how to solve them,
and they how to show the most difficult cases
and to solve their problem, et cetera.
So really, please continue with your video journal
for another many years.
Congratulations once again for the 40 year.
Osher: Thank you, Lucio.