The Data Lab has today released details of a new project using AI to assess and treat mesothelioma, or'asbestos cancer'. Scottish medical imaging software firm Canon Medical Research Europe and the University of Glasgow are set to publish clinical findings from a study evaluating a new cancer assessment tool, developed as part of the Cancer Innovation Challenge. A study team have created a prototype that can automatically find and measure mesothelioma on CT scans. These scans are then used by the trained AI to assess patient's response to drug treatments like chemotherapy. The AI was trained by showing it over 100 CT scans, on which an expert clinician had drawn around all areas of the tumour – showing the AI what to look for.
The key metric used by DRUML is drug response distance (D), which is computed using empirical markers of drug responses identified in the training set. The D metric is the difference in overall expression of markers increased in drug sensitive cells relative to markers increased in drug resistant cells within a sample. Because D is an internally normalized metric, obtained by subtracting averaged signals from two sets of phosphosites, proteins, or transcripts within a given sample, DRUML can use D to predict drug responses in a new cancer-derived sample without comparing it against a control or reference sample set.
Myelodysplastic syndrome (MDS) is a disease of the stem cells in the bone marrow, which disturbs the maturing and differentiation of blood cells. Annually, some 200 Finns are diagnosed with MDS, which can develop into acute leukemia. Globally, the incidence of MDS is 4 cases per 100,000 person years. To diagnose MDS, a bone marrow sample is needed to also investigate genetic changes in bone marrow cells. The syndrome is classified into groups to determine the nature of the disorder in more detail.
The Merriam-Webster dictionary defines artificial intelligence (AI) as "a branch of computer science dealing with the simulation of intelligent behavior in computers" or "the capability of a machine to imitate intelligent human behavior." The layman may think of AI as mere algorithms and programs; however, there is a distinct difference from the usual programs which are task-specific and written to perform repetitive tasks. Machine learning (ML) refers to a computing machine or system's ability to teach or improve itself using experience without explicit programming for each improvement, using methods of forward chaining of algorithms derived from backward chaining of algorithm deduction from data. Deep learning is a subsection within ML focussed on using artificial neural networks to address highly abstract problems;1 however, this is still a primitive form of AI. When fully developed, it will be capable of sentient and recursive or iterative self-improvement.
We are going to do binary classification, so the value of y (true/target) is going to be either 0 or 1. For example, suppose we have a breast cancer dataset with X being the tumor size and y being whether the lump is malignant(cancerous) or benign(non-cancerous). Whenever a patient visits, your job is to tell him/her whether the lump is malignant(0) or benign(1) given the size of the tumor. There are only two classes in this case. So, y is going to be either 0 or 1. Let's use the following randomly generated data as a motivating example to understand Logistic Regression.
Register for a free or VIP pass today. The past several years have made it clear that AI and machine learning are not a panacea when it comes to fair outcomes. Applying algorithmic solutions to social problems can magnify biases against marginalized peoples; undersampling populations always results in worse predictive accuracy. But bias in AI doesn't arise from the datasets alone. Problem formulation, or the way researchers fit tasks to AI techniques, can contribute.
TechRepublic's Karen Roby spoke with Jon Friis, CEO and founder of Miiskin, about how the Miiskin app is helping prevent skin cancer. The following is an edited transcript of their conversation. Karen Roby: We understand how technology can help change things in medicine, such as robots are in the operating room, and we're just seeing all kinds of really innovative things going on. Observing your moles is one of those things on our skin that I would never think technology would play a role in. Tell us before we get to the technology part of this, the augmented reality and machine learning.
To read the full story, subscribe or sign in. The rapidly expanding field artificial intelligence (AI)-aided image analysis received a boost with the FDA 510(k) clearance for Optellum Ltd.'s Virtual Nodule Clinic, which helps clinicians evaluate small, potentially malignant lung lesions or nodules. The action makes Optellum's system the first cleared radiomic application for early lung cancer, an area of active research for the last five years.
Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalised therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."
In order to overcome the challenges that stalled the use of AI in radiology, Yala used an adversarial machine learning approach. The algorithm works by deceiving the other in order to determine the differences in scores among radiology machines. This indicates that patients, who are in the same risk levels can show different scores. The model is also more accurate, owing to its ability to incorporated data from several different years.