Machine Learning Can Help Identify First Episodes of Schizophrenia, As Well As Treatment Response

MedicalResearch.com Interview with:

Bo Cao, Ph.D. Assistant Professor Department of Psychiatry Faculty of Medicine & Dentistry University of Alberta Edmonton

Dr. Bo Cao

Bo Cao, Ph.D.
Assistant Professor
Department of Psychiatry
Faculty of Medicine & Dentistry
University of Alberta
Edmonton

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Schizophrenia is a severe psychiatric disorder that comes with delusions, hallucinations, poor motivation, cognitive impairments.

The economic burden of schizophrenia was estimated at $155.7 billion in 2013 alone in the United States. Schizophrenia usually emerges early in life and can potentially become a lifetime burden for some patients. Repeated untreated psychotic episodes may be associated with irreversible alterations of the brain. Thus, it is crucial to identify schizophrenia early and provide effective treatment. However, identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Limited progress has been made in leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics.

In a recent study by Dr. Cao and his colleagues, they successfully identified the first-episode drug-naïve schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level by using a machine learning algorithm and the functional connections of a brain region called the superior temporal cortex.  Continue reading

Watson for Clinical Trial Matching Increases Enrollment in Breast Cancer Trials

MedicalResearch.com Interview with:

Alexandra Urman, MPH Clinical Research Manager Clinical Development IBM Watson Health 

Alexandra Urman

Alexandra Urman, MPH
Clinical Research Manager
Clinical Development
IBM Watson Health 

MedicalResearch.com: What is the background for this study? 

Response: Cancer statistics show only 3-5% of cancer patients participate in clinical trials although up to 20% may be eligible.

Dr. Tufia Hadad, a medical Oncologist at the Mayo Clinic in Rochester, Minnesota sought to address this issue and spearheaded a project conducted at the Rochester facility in collaboration with IBM Watson Health. The objective was to determine if the use of cognitive computing increased clinical trial enrollment and screening efficiency in the breast cancer clinic.

Watson for Clinical Trial Matching (CTM) is a cognitive system which utilizes natural language processing to derive patient and tumor attributes from unstructured text in the electronic health record that can be further used to match a patient to complex eligibility criteria in trial protocols.

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 Machines Learn To Cooperate With Human Partners, Who Often Cheat or Become Disloyal

MedicalResearch.com Interview with:

Jacob Crandall PhD Associate Professsor, Computer Science Brigham Young University 

Dr. Jacob Crandall

Jacob Crandall PhD
Associate Professsor, Computer Science
Brigham Young University 

MedicalResearch.com: What is the background for this study?

Response: As autonomous machines become increasingly prevalent in society, they must have the ability to forge cooperative relationships with people who do not share all of their preferences.  Unlike the zero-sum scenarios (e.g., Checkers, Chess, Go) often addressed by artificial intelligence, cooperation does not require sheer computational power.  Instead, it is facilitated by intuition, emotions, signals, cultural norms, and pre-evolved dispositions.  To understand how to create machines that cooperate with people, we developed an algorithm (called S#) that combines a state-of-the-art reinforcement learning algorithm with mechanisms for signals.

We compared the performance of S# with people in a variety of repeated games.

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How Do Stem Cells Respond To Diagnostic Radiation Studies?

MedicalResearch.com Interview with:
http://www.insilico.com/
Andreyan Osipov PhD
Insilico Medicine and
Dmitry Klokov PhD
Canadian Nuclear Laboratories, Chalk River, Ontario, Canada 

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Cells and tissues can be damaged when exposed to ionizing radiation. In case of radiotherapy, it is a desirable effect in tumor cells. In case of occupational, medical and accidental exposures, typically to low-dose radiation, this may pose health risk to normal cells and tissues.

In both cases, short-term assays that quantify damage to DNA and help evaluate long-term outcome are key to treatment/risk management. One such short-term assay is based on quantification of a modified histone protein called gH2AX in exposed cells up to 24 hrs after exposure. This protein marks sites in DNA that have both strands of the DNA helix broken or damaged. This assay is also widely used for various applications, including determination of individual radiosensitivity, tumor response to radiotherapy and biological dosimetry. With the advent of regenerative medicine that is based on stem cell transplantation, the medical and research communities realized that there is a need to understand how stem cells respond to low-dose diagnostic radiation exposures, such as CT scans. Stem cell therapies may have to be combined with diagnostic imaging in recipient patients. The gH2AX assay comes in very handy here, or at least it seemed this way.

We exposed mesenchymal stem cells isolated from human patients to low or intermediate doses of X-rays (80 and 1000 mGy) and followed formation of gH2AX in their nuclei. First we found that residual gH2AX signal in cells exposed to a low dose was higher than in control non-irradiated cells. If the conventional assumptions about this assay that it is a surrogate for long-term detrimental effects was followed it would mean that the low-dose exposed cells were at a high risk of losing their functional properties. So we continued growing these cells for several weeks and assayed gH2AX levels, ability to proliferate and the level of cellular aging. Surprisingly, we found that low-dose irradiated cells did not differ from non-irradiated cells in any of the measured functional end-points. This was in contrast to 1000 mGy irradiated cells that did much worse at those long-term end points.

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Deep Learning Algorithms Can Detect Spread of Breast Cancer To Lymph Nodes As Well or Better Than Pathologists

MedicalResearch.com Interview with:
Babak Ehteshami Bejnordi Department of Radiology and Nuclear Medicine Radboud University medical center, NijmegenBabak Ehteshami Bejnordi

Department of Radiology and Nuclear Medicine
Radboud University medical center, Nijmegen

MedicalResearch.com: What is the background for this study?

Response: Artificial intelligence (AI) will play a crucial role in health care. Advances in a family of AI popularly known as deep learning have ignited a new wave of algorithms and tools that read medical images for diagnosis. Analysis of digital pathology images is an important application of deep learning but requires evaluation for diagnostic performance.

Accurate breast cancer staging is an essential task performed by the pathologists worldwide to inform clinical management. Assessing the extent of cancer spread by histopathological analysis of sentinel lymph nodes (SLN) is an important part of breast cancer staging. Traditionally, pathologists endure time and labor-intensive processes to assess tissues by reviewing thousands to millions of cells under a microscope. Using computer algorithms to analyze digital pathology images could potentially improve the accuracy and efficiency of pathologists.

In our study, we evaluated the performance of deep learning algorithms at detecting metastases in lymph nodes of patients with breast cancer and compared it to pathologist’s diagnoses in a diagnostic setting.

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AI Can Be Embedded With Universally Accepted Human Biases

MedicalResearch.com Interview with:

Aylin Caliskan PhD Center for Information Technology Policy Princeton University, Princeton, NJ

Dr. Caliskan

Aylin Caliskan PhD
Center for Information Technology Policy
Princeton University, Princeton, NJ

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Researchers have been suggesting that artificial intelligence (AI) learns stereotypes, contrary to the common belief that AI is neutral and objective. We present the first systematic study that quantifies cultural bias embedded in AI models, namely word embeddings.

Word embeddings are dictionaries for machines to understand language where each word in a language is represented by a 300 dimensional numeric vector. The geometric relations of words in this 300 dimensional space make it possible to reason about the semantics and grammatical properties of words. Word embeddings represent the semantic space by analyzing the co-occurrences and frequencies of words from billions of sentences collected from the Web. By investigating the associations of words in this semantic space, we are able to quantify how language reflects cultural bias and also facts about the world.

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AI plus Genetic Database Drives Personalized Cancer Treatment

MedicalResearch.com Interview with:

Dr. Kai Wang Zilkha Neurogenetic Institute, University of Southern California Institute for Genomic Medicine, Columbia University

Dr. Kai Wang

Dr. Kai Wang
Zilkha Neurogenetic Institute, University of Southern California
Institute for Genomic Medicine, Columbia University

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Cancer is a genetic disease caused by a small number of “driver mutations” in the cancer genome that drive disease initiation and progression. To understand such mechanism, there are increasing community efforts in interrogating cancer genomes, transcriptomes and proteomes by high-throughput technologies, generating huge amounts of data. For example, The Cancer Genome Atlas (TCGA) project has already made public 2.5 petabytes of data describing tumor and normal tissues from more than 11,000 patients. We were interested in using such publicly available genomics data to predict cancer driver genes/variants for individual patients, and design an “electronic brain” called iCAGES that learns from such information to provide personalized cancer diagnosis and treatment.

iCAGES is composed of three consecutive layers, to infer driver variants, driver genes and drug treatment, respectively. Unlike most other existing tools that infer driver genes from a cohort of patients with similar cancer, iCAGES attempts to predict drivers for individual patient based on his/her genomic profile.

What we have found is that iCAGES outperforms other tools in identifying driver variants and driver genes for individual patients. More importantly, a retrospective analysis on TCGA data shows that iCAGES predicts whether patients respond to drug treatment and predicts long-term survival. For example, we analyzed two groups of patients and found that using iCAGES recommend drugs can increase patients’ survival probability by 66%. These results suggest that whole-genome information, together with transcriptome and proteome information, may benefit patients in getting optimal and precise treatment. Continue reading