clinical image data to excel

7 proven ways of enhancing clinical trials thanks to Legit.Health

Table of Contents

    Introduction

    Clinical trials are the engine that pushes forward medical science. What comes out as obvious is that the tools at the disposal of those at the forefront of progress should be as advanced and cuting-edge as the research that is been worked on.

    Legit.Health takes up the challenge of providing researchers with a modern, useful and reliable tool for their clinical trials by incorporating deep learning algorithms and image analysis technology into an easy-to-use and well-designed interface.

    The research that will propel us to the future shouldn’t take place using tools of the past.

    Taig Mac Carthy, COO at Legit.Health

    How does Legit.Health help you during clinical trials?

    Aside from being an incredible tool for the daily clinical practice of next-generation doctors around the world, Legit.Health also provides an era-defining approach to clinical trials by granting coordinators the ability to deploy a wide range of new technologies based on artificial intelligence.

    Legit.Health helps effective therapies to reach patients faster by increasing both the quality and quantity of endpoints in any given protocol, greasing the wheels of science so the process of determining the efficacy of a new world-altering drug is as easy as possible for the researchers.

    What incredible capabilities does this software offer?

    Px2Csv

    Px2Csv

    Pixel-to-CSV conversion

    Turns the visual information detected by algorithms and the metadata of the image into rows of data, that can be exported into CSV, Excel, JSON or any other spreadsheet solution.
    APROM algorithms

    APROM algorithms

    Automatic Patient-Reported Outcome

    Automatically fill in most of the clinical scoring systems, by looking at visual signs contained in images, such as desquamation, dryness, erythema, surface, lesion count, and so on.
    MIC Algorithms

    MIC Algorithms

    Match Inclusion Criteria

    Automatically screen patients and include or exclude cases that do not fit the protocol’s inclusion criteria, either because the condition is not present – or the severity is too high or too low for the study.
    DIQA algorithms

    DIQA algorithms

    Dermatology Image Quality Assurance

    Automatically check images right when they are taken and ensures that they have enough quality for them to be useful. If an image has a mistake, DIQA prompts the user to fix that specific issue.
    ACA Module

    ACA Module

    Adverse Condition Alerts

    Scans images taken by patients for conditions flagged as adverse. For instance, malignancy and pre-malignancy. If an adverse condition is detected, alerts the researchers.
    QoL & Burden module

    QoL & Burden module

    Life Quality and Burden of Disease module

    Allows the gathering of PROMs such as DLQI, and more specific life quality indexes such as CU-QoL, AKQoL, and many more. It also accepts custom questionnaires with a form builder.

    Speed up the pathology reporting process and improve the patient’s quality of life.

    The best tool for decentralized clinical trials (DCT)

    As COVID-19 began to spread around the world, research centres and pharmaceutical companies conducting clinical trials had to quickly adopt remote data collection technologies and processes to keep patients safe and clinical trials running.

    One of the biggest challenges for these new types of clinical trials is to be compliant with the current regulations, while keeping the patient more engaged than ever to obtain accurate and reliable data. Luckily, Legit.Health has developed the perfect tool to overcome these challenges.

    Being able to be plugged into any established DCT management software such as Medable or Apple ResearchKit, Legit.Health is the prime APROM (Automatic Patient-Reported Outcome Measures) tool in the market, as it allows its users to run an efficient decentralized clinical drug development and enables screening at a larger scale.

    Prevents low-quality images

    In decentralized clinical trials or studies that require photographic evidence of the patient’s recovery process, faulty or low-quality images can hinder the progress of the study.

    Legit.Health‘s revolutionary image quality assurance algorithms increase the overall quality of the recorded images by turning an ordinary smartphone into a clinically reliable image capture device, enabling decentralized clinical trials and empowering the patient to report on the condition autonomously.

    This novel deep learning algorithm achieves this by checking the quality of the image before considering it for the trials, and, upon detecting a quality drop or a problem, prompts the user to fix it before taking another picture.

    More reliability thanks to Automatic PROMs algorithms

    Legit.Health’s next-generation algorithms are able to automatically fill in most of the clinical scoring systems by analysing symptoms visible in pictures such as desquamation, dryness, erythema, affected area or lesion count, among others.

    This not only reduces the possible errors of reporting, but also makes the job of the Data Manager easier, as most of the gruelling routine work becomes automated.

    In addition, the algorithms provide higher reliability and greater accuracy in data collection, since there is no time difference between the recording of the lesion and its actual condition, and significantly reduces both the inter-observer and intra-observer variability.

    Lastly, the assistance of the machine makes scaling the trials trivially easy, as it bridges the gap between languages, countries, or even brands.

    Speed up the pathology reporting process and improve the patient’s quality of life.

    Automated match inclusion criteria and adverse condition detection

    The algorithms will automatically exclude patients who do not fit the protocol’s inclusion criteria, either because the severity is at odds with the goals of the study or because the condition does not match the trial.

    This allows online patient recruitment to become a viable option, thus expanding the potential pool of patients available for clinical trials.

    Additionally, the revolutionary diagnosis assistance algorithms of Legit.Health will detect any situation that might be deemed dangerous, such as a disease with a high probability of scaling up or a lesion that may present malignancy, and will report to the researchers that the patient needs medical attention.

    Helps researchers extract all the information from a picture

    One of the biggest constraints of any clinical trial, especially one run remotely, is how time-consuming it is to analyse every image and the expertise needed to turn a picture into actual user data. This becomes apparent when you consider the usual schedule of a doctor and how little time they have to devote themselves to data entry tasks.

    Legit.Health‘s technology turns automatically any dermatological picture into raw data, extracting the information hidden in the pixels and turning it into values such as redness, area, severity, dryness, desquamation, and many more.

    Visual explanation of pixel to csv (px2csv) algorithms
    Visual explanation of pixel to csv (px2csv) algorithms

    This translates into a significant reduction in the workload for the data manager since this process is automatic, as well as to greater reliability of clinical endpoints at a decreased cost. The absence of any human bias in the algorithms zeroes out the intra-observer variability.

    Additionally, this will suppose a massive expanse in scope to clinical trials. Up to the present time, most researchers limit themselves to a couple of endpoints in any given study, as they have to balance the needs of the research with the budgetary and time limitations.

    Thanks to Legit.Health, measuring 2 variables costs the same as measuring 100, which exponentially increases the number of clinical endpoints and allows researchers to achieve greater granularity in their data.

    Speed up the pathology reporting process and improve the patient’s quality of life.

    Encourages patient to adhere to the protocol

    Legit.Health‘s revolutionary approach to clinical trials does not only rely on the cutting edge algorithmic technology to do all the heavy lifting. Its slick design, focused on ease of use and readability, considers the day-to-day realities of the patients to help them adhere to the protocol of the clinical trial.

    By creating tasks for the patient, providing them with alerts and reminders, rewarding their reporting with principles extracted from the ideas of gamification or providing them with useful information about their disease, Legit.Health increases patient adherence, enriches endpoint diversity and enables patient-centric clinical trials.

    Easy to include quality life indexes

    Legit.Health seamlessly incorporates the main life quality indexes such as DLQI, CU-QoL or AKQoL, among others, allowing the clinical trials to enrich the data they collect and give it texture, as well as provide it with additional context.

    This supposes no extra work for the researchers, as the app has both the questionnaires and the accessibility to interpret that data built-in, so researchers don’t need to add any step to the data collection process.

    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.

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    7 proven ways of enhancing clinical trials thanks to Legit.Health
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