2 publicaciones etiquetados con "Artificial Intelligence"

Read the paper​

We invite you to read the full paper for yourself, or you can scroll down to read the summary in this educational blog post.

Introduction​

We introduce AUAS, an automatic equivalent of UAS that deploys a deep learning lesion-detecting algorithm, called Legit.Health-UAS-HiveNet. Our results show that our algorithm assesses the severity of Chronic Urticaria cases with a performance comparable to that of expert physicians.

Furthermore, the algorithm can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials.

The real impact of the Legit.Health-UAS-HiveNet in clinical practice resides on the power to support physicians during not only the diagnostic process but also in the monitoring of patients with chronic types of urticaria, by helping them prescribe treatments and increase the adequacy of treatments.

Regarding clinical trials, the AUAS has the potential of becoming a new clinical endpoint that could increase both the quality and the quantity of data available to researchers.

Authors of the publication​

What is urticaria severity scoring?​

Urticaria is a very common disease characterized by erythematous, edematous, itchy, and transient plaques that involve the skin and mucous membranes. It can be classified into subtypes such as acute spontaneous urticaria, chronic spontaneous urticaria, chronic inducible urticaria, and episodic chronic urticaria.

Diagnosis of chronic urticaria is usually performed through clinical observation. In other words: the assessment of the disease's severity is performed through manual scoring systems that are filled in subjectively.

The most commonly used scoring system is the Urticaria Activity Score (UAS), which can also be used for 7 consecutive days, in which case it is referred to as UAS7.

The problem with visual scoring​

The most indisputable limitation of manual scoring systems is the inherent difficulty of human beings to quantify parameters in an objective, stable and precise way.

Humans have a limited ability to count hives, quantify the surface area of a lesion or quantify the redness of an area. This human limitation in parameter estimation is also reflected in the effort and time required to complete the urticaria activity questionnaires, which end up being a very unrewarding task for patients and may result in poor adherence.

On the other hand, scoring systems classify disease severity using a limited range of scores, with three or four categories, such as: none, mild, moderate and severe in the case of the UAS. Indeed, questionnaires have a very high minimum detectable change, as they are discrete ranges rather than continuous scales.

And finally, these questionnaires are susceptible to bias. This is especially true in cases where the patient knows that the treatment they receive will be determined by the information they provide. And due to the asynchronous nature of the reported measure, the clinical team lacks the means to ensure that the values reported by the patient are chronologically accurate or simply truthful, which precludes external verification.

The goal of the Automatic UAS​

In this work, we propose the Automatic Urticaria Activity Score (AUAS), an automatic version of the objective part of the UAS that applies convolutional neural networks to count hives automatically with high precision.

The goal is to assist clinicians in filling scoring systems such as the UAS in a more objective way and quicker, which could improve health outcomes and provide high-quality endpoints to measure the effectiveness of the treatments for urticaria.

The solution​

We trained trained a hive-counting neural network called Legit.Health-UAS-HiveNet.

To make artificial intelligence accessible to the healthcare professional, we developed a fully integrated CADx system, a web application that integrates Legit.Health-UAS-HiveNet algorithm and calculates the patient-based UAS by looking at images taken with smartphone cameras.

The CADx system works in three stages: image and itchiness input, processing of the images, and the creation of a report with the severity assessment.

The report can also combine the scores of multiple images uploaded in the same day to provide the global AUAS score.

In other words: if the user uploads pictures of several body parts, the report of the CADx system shows both the local and the global AUAS scores. The global AUAS is calculated by summing the results of all the images processed by the CADx system.

Conclusion​

In this work, we have presented the AUAS, the first artificial intelligence that automatically fills in the UAS scoring system by looking at smartphone images. The main advances in this algorithm are reducing the time spent by patients in filling in the manual severity scoring system and standardizing urticaria assessment with reduced inter-observer variability and higher reliability.

We were able to overcome clinical assessment variability by developing a merging algorithm that fuses all experts' annotations to create a consensus.

The AUAS as a scoring system presents improved clinimetric properties, but it also carries the advantage of providing a picture of the lesion along with the severity score, which allows researchers greater oversight of studies. In conclusion, we believe that the AUAS and Legit.Health-UAS-HiveNet has the potential to improve health outcomes, reduce costs, and increase the practice of evidence-based medicine in health organizations.

Get access now​

This free 23-day trial of Legit.Health gives clinics and hospitals a hands-on look at how to drive increased adherence and improve patient outcomes, as well as improving efficiency and overall quality of life.

The Comillas ICADE Law School held the 1st Telemedicine Conference within the Uría Menéndez-ICADE Chair on Market Regulation. The Conference had the participation of representatives of the public and private sectors who have had the opportunity to review the situation of telemedicine in our country.

Relevant issues such as ethical, data protection and regulatory or responsibility aspects of telemedicine have been reviewed, concluding on the feasibility and necessity of the practice. Likewise, the essential public-private collaboration to promote the digitization of health has been highlighted as one more lever than those envisaged in the Recovery, Transformation and Resilience Plan financed with NextGenUE funds.

In the different round tables, an interesting tour has been made on the state of the practice of telemedicine both in the private sector, as well as in the public health and social health sector.

Speakers​

The speakers highlight the contribution of telemedicine in the crisis derived from the COVID-19 pandemic, and agree on the potential that the digitization of healthcare services offers. Telemedicine must be consolidated as the definitive impulse for the digitalization of the health system.

Dr Ricardo Ruiz (ICD)​

Dr. Ricardo Ruiz, medical director of the International Dermatology Clinic (CDI), first Spanish center where clinical, surgical, oncological, paediatric and restorative dermatology is carried out jointly. He aims to offer the highest scientific rigour and provide his patients with a unique quality of care.

Taig Mac Carthy (Legit.Health)​

As Co-founder and COO of Legit.Health, Taig Mac Carthy seeks to offer dermatologists the keys to enhancing their professional experience. Developing, always with a focus on data protection and privacy, the perfect tool for a user experience and usability.

Andres Padilla (Movistar Salud)​

With solid experience in the world of technology and telecommunications, Andrés Padilla, is an expert on understanding customer needs, market trends and emerging technologies. He seeks to offer medical solutions thanks to the facilities and modernity offered by Movistar telemedicine service, which allows patients to be connected to a doctor wherever they are.

Next Generation Dermatology​

We are in the middle of the artificial intelligence revolution. Created to facilitate and modernize dermatologist's work, Legit.Health algorithms ensure quality medical care. The speed of the automatic results brings easier disease monitorization, with complete security and data protection, making this software one of the best tools available nowadays.

Legit.Health gives doctors the ability to telematically and rapidly track skin lesions, providing automated monitoring of changes during the course of the disease, which are often invisible to the human eye. Offering dermatologists all the tools that they need to empower their patients to take control of their disease and not the other way around.

Telemedicine was born to stay and bring with it the opportunity to generate new platforms that can help save lives. This is why Legit.Health is so advantageous and necessary for dermatologists, because it provides them a new way for interacting with patients, ensuring them a good monitoring of their pathology, inside and outside the clinic.

Legit.Health technology is at the service of doctors who only have to worry about making the diagnosis of the patient. Helping them to make a better use of their time, removing the unnecessary follow-up visits on their consults, and providing an error-free evaluation, speeding up the cure of their patients' disease.

From home, patients only need to take their smartphones and take a photo of the spot, welt or redness of their skin and fill out a short medical questionnaire. Thanks to Artificial Intelligence, the tool objectively communicates to the doctor the type of pathology, the patient's condition and the changes in the lesion. Putting the patient in the foreground, much more informed and sure of his illness.

This is the beginning of the dermatology revolution, a new generation of dermatologists, like Dr. Ricardo Ruiz Rodriguez, medical director of the CDI clinic. Professionals like them are looking to take advantage of the opportunities they have around them to continue growing as doctors and learning new techniques and tools to improve the lives of their patients, without the need to see patients on site.

A vision of the future​

Global health management, during the pandemic, has proved that we are still lacking in resources in terms of medical care.

Cancelled medical tests, postponed consultations, treatments without follow-up... Many patients have had limited access to the health system because resources had to be used to tackle an unprecedented health crisis. Thousands of patients with chronic diseases had to reduce or even cease their treatment because they had no place in medical centers. A very problematic situation for skin diseases such as melanoma, since a delay in the follow-up of the pathology reduces considerably the life expectancy of the patient.

This is why digital telemedicine platforms are here to stay. Specifically, Legit.Health Artificial Intelligence ensures quality medical care, providing specialized follow-up to those patients who need to have their disease under control.

Equipped with machine-learning algorithms, Legit.Health software provides dermatologists with the perfect tool for their consultations, offering them a vision of the future, with many tools and mechanisms to take their practice to another level. With reliable results of up to 83% in skin diseases such as dermatitis and 94% in melanoma.

By simply analyzing images taken through any conventional smartphone, Legit.Health algorithms are capable of automatically filling in the scoring systems of up to 232 skin diseases. Such as UAS7, PASI, SCORAD, BSA or DLQI among many others. This greatly speeds up the work of professionals, who only have to worry about offering an evaluation to their patients in the form of an easy and simple diagnosis.

The future of healthcare also demands that patients take a more active role in caring for their own illness. Using tools that they can integrate into their everyday life. Legit.Health achieves greater patient empowerment in a systematic way that allows them to assess advances in their treatments and help doctors to document the process of their disease. Patients can report their pathologies just when they need it, and algorithms ensure that the doctor gets the right information, in the right way, and at the right time.