Artificial intelligence (AI) is becoming more common for screening, diagnosing and helping deal with eye conditions. This innovative technology is already being used in online web search tools like Google, speech recognition tools and other smart devices and gadgets. Now, currently, AI is showing promises in healthcare.
With all its abundant potential, Artificial Intelligence might seem like a technological leap for humanity, but clearly, it’s only our latest step in improving the way in which we see the world.
The human eye has once again become a window for technological innovation in healthcare. This time for artificial intelligence (AI), with neural networks looking out for patterns when fed with data. Such networks had been in use for decades, however today, they have resulted in an explosion of applications. AI is on a roll. And since that eyes have always been amenable to images and photography they have become data troves for algorithms seeking patterns and symptoms of diseases. With the eyes have always been responsible for one of the 5 critical senses, it is one of the most complex organs of the human physique, and our survival also has always been depended on our ability to detect danger through our critical senses.
An outcome from several studies shows that the AI has the potential to help doctors detect and diagnose the eye diseases. However advance study and research are required to show the technologies to do what they set out to do. It’s going to take some effort to earn ophthalmologists trust and to convince them to use AI-centered tools in their practices.
Advances in optical technology over the centuries:
Over the centuries, a lot of limitations were overcome by advanced study and research in optical technology. Culminating in the invention of eyeglasses in the early 1300s which addressed the challenge of poor vision which in effect cause farsightedness, astigmatism, nearsightedness.
Eyeglasses further enabled humans to take part in and make a contribution of their gathered experiences to the collective skills-base even in the latter a part of their lives, when age-deduced vision loss occurs.
The lens was again utilized in the subsequent inventions like the microscope and the telescope in the early 1600s. These inventions have enabled humans ability to see at the micro level and extraterrestrial objects with clarity which was not feasible with the bare eye.
These inventions also helped spur numerous biological and geographical discoveries, both on earth and extraterrestrial, additional growing the scientific knowledge.
The invention of frequencies outside the visual spectrum for the period of the late 1800s, for illustration, led to the wave of radiological innovations, such as ultrasounds and X-Rays, that leveraged virtually the whole electromagnetic spectrum to see via animate and inanimate objects.
Now again, with the invention of the computer in late 1930’s and further improvements in computing hardware and artificial intelligence and machine learning algorithms, humans now are capable to gain insights that are not possible to collect with simply human eyes, even where the photos and videos are of the visual spectrum, or making use of any of our previous inventions.
All the above advancements have come to head into humanity harnessing technology. It also amplified our perception and imagination to see beyond the physically obvious.
Present Stage of Artificial Intelligence:
● Artificial Intelligence Excels at image consciousness
AI has been growing in popularity as photo evaluation is fundamental to health problem prognosis and medicine. Specialties like radiology, pathology, dermatology, and ophthalmology are foremost main AI study.
Researchers have already got validated AI-centered methods that use
photographs of the retina to admire patients at a chance for cardiovascular disease, and x-ray pics to aid identify pediatric pneumonia.
● Cameras to realize Diabetic Retinopathy
People with diabetes are at chance for diabetic retinopathy — a probably blinding eye ailment.
The IDx-DR is the first FDA-authorized AI-based device for detecting diabetic retinopathy. Principal care physicians and different healthcare specialist can use it. IDx-DR analyzes photos all for a retinal digicam. The application tells the medical professional if a patient should see an ophthalmologist for possible treatment.
● Application to admire Macular Degeneration
Macular degeneration explains a relevant vision loss and is most often no longer seen until imaginative and prescient could be very blurry. A February 2018 study journal confirmed that artificial intelligence-founded application which might recognize early symptoms of macular degeneration.
We’re nonetheless in the early stages of Artificial Intelligence and at best just starting to realize its full variety of benefits and challenges. That said, it’s however yet another step forward humanity and now not unreasonable to expect it will have an impact on our fine lifestyles to be as enormous if not exceed that of all our prior creative and prescient-related technological developments.
● Computer vision and Multimedia research by IBM:
IBM Research is a leading player in this technology with the quest to offer AI system sight. They are enabling IBM’s AI platform, Watson, to interpret visual content without any difficulty as it does with text.
IBM scientists are these days building a compact hyper imaging platform that “sees” across separate quantities of the electromagnetic spectrum in a single platform to potentially permit a bunch of sensible and low-cost gadgets and programs. A hyper image of a pharmaceutical drug or a financial document could tell us what’s fraudulent and what’s not. What was once beyond human notion will become visible with this technology.
IBM scientists have claimed that within Five years new imaging devices build on the basis of hyper imaging technology and Artificial Intelligence(AI) will help us see broadly beyond the domain of visible light. This will be done by combining multiple bands of the electromagnetic spectrum to disclose valuable insights or potential risks that may otherwise be unknown or hidden from our sight.
Most importantly, these gadgets and devices might be portable, inexpensive and easily accessible and obtainable, so superhero vision may also be part of our day-to-day experiences.
import cv2 as cv
import numpy as np
from common import *
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument(‘–zoo’, default=os.path.join(os.path.dirname(os.path.abspath(__file__)), ‘models.yml’),
help=’An optional path to file with preprocessing parameters.’)
parser.add_argument(‘–input’, help=’Path to input image or video file. Skip this argument to capture frames from a camera.’)
parser.add_argument(‘–framework’, choices=[‘caffe’, ‘tensorflow’, ‘torch’, ‘darknet’],
help=’Optional name of an origin framework of the model. ‘
‘Detect it automatically if it does not set.’)
parser.add_argument(‘–backend’, choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help=”Choose one of computation backends: ”
“%d: automatically (by default), ”
“%d: Halide language (http://halide-lang.org/), ”
“%d: Intel’s Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), ”
“%d: OpenCV implementation” % backends)
parser.add_argument(‘–target’, choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help=’Choose one of target computation devices: ‘
‘%d: CPU target (by default), ‘
‘%d: OpenCL, ‘
‘%d: OpenCL fp16 (half-float precision), ‘
‘%d: VPU’ % targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, ‘classification’)
parser = argparse.ArgumentParser(parents=[parser],
description=’Use this script to run classification deep learning networks using OpenCV.’,
args = parser.parse_args()
args.model = findFile(args.model)
args.config = findFile(args.config)
args.classes = findFile(args.classes)
# Load names of classes
classes = None
with open(args.classes, ‘rt’) as f:
classes = f.read().rstrip(‘\n’).split(‘\n’)
# Load a network
net = cv.dnn.readNet(args.model, args.config, args.framework)
winName = ‘Deep learning image classification in OpenCV’
cap = cv.VideoCapture(args.input if args.input else 0)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
# Create a 4D blob from a frame.
inpWidth = args.width if args.width else frame.shape
inpHeight = args.height if args.height else frame.shape
blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
# Run a model
out = net.forward()
# Get a class with a highest score.
out = out.flatten()
classId = np.argmax(out)
confidence = out[classId]
# Put efficiency information.
t, _ = net.getPerfProfile()
label = ‘Inference time: %.2f ms’ % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
# Print predicted class.
label = ‘%s: %.4f’ % (classes[classId] if classes else ‘Class #%d’ % classId, confidence)
cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
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