Know the Technological Advances That Allow Early Diagnoses

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In recent decades, electronic devices have become deeply rooted in our daily lives. This continuous advancement of technology does not only include smartphones or global connectivity thanks to the Internet but also, thanks to the application of electronic engineering and biotechnology, new techniques have been developed that can transform and even benefit the lives of beings humans.
Known as AI or artificial intelligence, this technology has many fields of application in medicine, from the communication of people with neural problems through computers, to the manufacture of prostheses for people with physical disabilities, and even improvement in the diagnostic processes of some diseases. Such is the case with melanomas.

Melanomas are the most serious skin cancer, which if not detected early, can spread to other organs and cause death. It develops in melanocytes, cells responsible for pigmenting our skin.
Basic analysis is done visually from pigmentation (SPL, Skin Pigmentation Level), using magnifying lenses with specific lights (dermatoscopy). Abnormal pigmentation can be underestimated or diagnostic times can be lengthened, which would imply a risk for the patient.Preliminary diagnoses are made through imaging, using MRIs, CT scans, and even X-rays. Also biopsies.

It is in this context that by applying AI, some researchers and study centers such as MIT have developed a series of convolutional neural network (DCNN) conduits, which allow images to be classified in a specific way using multidimensional arrays and algorithms; These networks are similar to the human visual cortex but with a multiplied image reception, which allows a deeper and wider field.
Widefield photographs can determine lesions and mark them in a heat map format, quickly detecting possible melanomas.
This would allow them to be treated in time and save many lives; This is ratified by Luis R. Soenken, the first author of an article published in Science Translational Medicine.

Soenksen is a pioneer of AI in the medical field and has a postgraduate degree.
Along with James J. Collins, Termeer Professor of Medical Engineering and Biological Sciences and Engineering, W. Kieckhefer Professor of Health Sciences and Technology, and Martha J. Gray, Professor of Electrical Engineering and Computer Science, faculty members from the Institute of Engineering and Medical Sciences (IMES) of MIT, has carried out a series of investigations, using wide-field images, including 20,388 of 133 patients at the Gregorio Marañon Hospital in Madrid.
The process resulted in an efficiency of 90.3% SPL using the DCNN system, notably minimizing individual visual diagnostic times.

Gray explains that the project was developed with fellows from the Catalyst program belonging to the MIT HST/IMES, the main objective is to put science at the service of human health.
The work was supported by the Abdul Latif Jameel Clinic and the Ministry of Education, Youth and Sports of the Community of Madrid, through the Madrid-MIT M+Vision Consortium.