Thirona, a Dutch AI medical image analysis company, has licensed a patent allowing it to develop an AI algorithm that helps identify abnormalities in CT lung scans, ensuring early detection of Cystic Fibrosis (CF). The company will use the license to integrate a quantitative method for the analysis of chest CT scans as well

With only approximately 70,000 known cases every year, CF is a rare genetic disorder. Current clinical methods to detect the disease are both time consuming and require extensive training. Thirona lets clinicians automatically detect and quantify lung disease, improve care, and enhance clinical decision-making

This is a competitive space. One of Thirona’s competitors, NuvoAir recently shared three key pulmonology related studies presented at the 2020 North American Cystic Fibrosis (CF) conference showing that patients with the respiratory disease can achieve good quality spirometry at home through their platform

“Artificial intelligence algorithms make medical image analysis easier, more accurate and more effective. But it is hard work,” said Dr Eva van Rikxoort, managing director of Thirona. Our algorithm quickly and accurately detects “bronchiectasis and mucous quantifications in very young patients with CF, making personalized treatment easier. We believe this will have a substantial impact for patients and are proud to be part of the process,” she added.

What is Atrial Fibrillation?

Atrial Fibrillation is a heart condition that causes irregular and often a fast heart rate. It’s unclear exactly what causes it, but we do know that it’s common in patients with other existing heart conditions.

How Are Wearables Helping with Detection?

On May 16, 2019, Preventice Solutions presented clinical data validating its BodyGuardian Remote Monitoring System with the BeatLogic deep learning platform at Heart Rhythm 2019 – the Heart Rhythm Society’s (HRS) 40th Annual Scientific Sessions in San Francisco.

This technology leverages machine learning and artificial intelligence (AI) for detection of atrial fibrillation (AF) and was validated using clinician adjudicated data. The system is designed to create a constant connection to monitor cardiovascular data in patients in their daily lives.

Today, AI, machine learning, and deep learning tools allow physicians to manage the massive amount of data produced on wearables and other sensor technology to track patient data, then diagnose health problems like AF, arrhythmia and hemodynamic instability.

What Does This Mean for Healthcare?

Without these AI technologies in healthcare, it would be impossible for physicians to manage and review the massive amounts of data collected on patients by wearables. With AI accurately identifying atrial fibrillation episodes, physicians can focus on the unique treatment for each patient.

Results from the study show how BeatLogic accurately detects the beginning and end of arrhythmias, ensuring accurate burden calculations while maximizing clinical value.

The platform leverages multiple deep neural networks to detect AF episodes at rates that meet or exceed the best reported values within the literature. Perfect detection performance was achieved for AF episodes lasting more than one minute.

Wearable patch electrocardiogram (ECG) monitoring devices combined with deep learning algorithms help detect the beginning and end of AF episodes, providing physicians with important clinical context for determining the appropriate treatment approach.

On June 21, 2021, Sensome, a pioneer in connected medical devices, announced that its Clotild^® Smart Guidewire System achieved the Breakthrough Device designation by the Center of Devices and Radiological Health (CDRH) of the Food and Drug Administration (FDA). The revolutionary sensor technology uses machine learning algorithms to instantly identify biological tissues while turning invasive medical devices into connected healthcare devices.

Although ischemic stroke is a leading cause of long-term disability it can be treated by removing the clot blocking the brain blood vessel, but the clot needs to be removed as quickly as possible. Without knowing a clot’s unique characteristics, Clotild^® Smart Guidewire System can remove the clot on the first attempt in one out of three cases. Its AI-powered tissue sensor provides physicians with critical information about the clot.

The FDA Breakthrough Devices Program is a voluntary program for medical devices that provide more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions. It is intended to help patients receive more timely access to breakthrough technologies and, in 2021, other breakthrough technologies have included batteries, implants, catheters, and ultrasound equipment. As part of this program, the FDA will provide Sensome with priority review and interactive communication on its path from device development to commercialization.

Lung cancer is the deadliest cancer in the world and about 75% of those are diagnosed with it will die within five years of discovery. Most patients are only diagnosed after the disease has advanced to the serious Stage III or IV level. However, if tumors are small and confined to the lung, the survival rate increases substantially. The need for early detection is clear and AI is powering the development of systems that can detect ever-smaller lung tumors.

Optellum, a lung health company aiming to redefine early diagnosis and treatment of lung disease, has received FDA 510(k) clearance for Virtual Nodule Clinic, a revolutionary AI-powered clinical decision support system that helps pulmonologists and radiologists uncover early stages of lung cancer. This is the FDA’s first application of AI decision support for early lung cancer diagnosis.

The world’s first FDA-cleared imaging AI/” Radiomics”-based digital biomarker for lung cancer, Virtual Nodule Clinic helps pulmonologists identify and track at-risk patients with suspicious lung nodules, then make optimal clinical management decisions for them. The software provides a clinically validated Lung Cancer Prediction (LCP), which is computed from full patterns of 3D pixels in standard CT image scans.

Use of the Virtual Nodule Clinic dramatically improves diagnostic accuracy and clinical decision-making. In the clinical study underpinning the FDA clearance, all readers in the study demonstrated a statistically significant improvement in diagnosing lung nodules when using the system, which consistently outperformed conventional risk prediction models. In an independent validation study, the AI correctly reclassified indeterminate nodules into high- and low-risk categories in more than a third of cancers and benign nodules, demonstrating the potential to speed up lung cancer diagnosis and reduce invasive biopsies and surgeries on patients without lung cancer.

Today’s AI systems utilize deep learning systems analyze real-world tumor examples and evaluate which constitute a problematic one, rather than looking for pre-defined features. The AI system is given a large data set of lung CT scan images, some with cancer and some without, and the machine learns what a lung cancer nodule looks in extraordinary detail. These systems can help radiologist overcome the limits of human vision, which can miss tiny malignant lesions that signify cancerous growth. Currently, up to 35% of lung nodules go unnoticed during initial screenings and AI systems can both discover hard to find lung spots as well as reduce the burden of overwhelming caseloads on busy specialists.

These developments promise to make lung-cancer screenings more precise and accessible to all, with AI should be leading the way for all.

FDA clearance 510k, pulmonologists, radiology, Lung Cancer Prediction, lung cancer treatment, artificial intelligence, AI, machine learning, lung cancer diagnosis, Virtual Nodule Clinic.