Goto

Collaborating Authors

Results


Toward a disease-sniffing device that rivals a dog's nose

#artificialintelligence

Numerous studies have shown that trained dogs can detect many kinds of disease -- including lung, breast, ovarian, bladder, and prostate cancers, and possibly Covid-19 -- simply through smell. In some cases, involving prostate cancer for example, the dogs had a 99 percent success rate in detecting the disease by sniffing patients' urine samples. But it takes time to train such dogs, and their availability and time is limited. Scientists have been hunting for ways of automating the amazing olfactory capabilities of the canine nose and brain, in a compact device. Now, a team of researchers at MIT and other institutions has come up with a system that can detect the chemical and microbial content of an air sample with even greater sensitivity than a dog's nose.


Artificial Intelligence Powers a Disease-Sniffing Device That Rivals a Dog's Nose

#artificialintelligence

Andreas Mershin visits with one of the trained disease-sniffing dogs in his office at MIT. The dogs are trained and handled in the UK by the organization Medical Detection Dogs. Trained dogs can detect cancer and other diseases by smell. A miniaturized detector can analyze trace molecules to mimic the process. Numerous studies have shown that trained dogs can detect many kinds of disease -- including lung, breast, ovarian, bladder, and prostate cancers, and possibly Covid-19 -- simply through smell.


Dogs that can smell prostate cancer could inspire 'robotic noses'

Daily Mail - Science & tech

Dogs that can smell prostate cancer could inspire'robotic noses' to sniff out the disease, in a technique dubbed'machine olfaction', a new study reveals. In a pilot study, British and US researchers trained dogs to detect aggressive prostate cancer from people's urine samples. Dogs have an extremely sensitive sense of smell and can pick up on'volatile organic compounds' (VOCs) released during the early stages of many cancers. The scientists then used the data to create an artificial neural network that could detect the cancer-specific chemicals that the dogs could smell. The hope is that the dogs' performance can eventually be replicated and used in technology such as an app on a smartphone.


Using Artificial Intelligence to Improve Prostate Biopsies - Docwire News

#artificialintelligence

Researchers from Google Health found that using artificial intelligence (AI) to aid in the review of prostate biopsies improved the quality, efficiency, and consistency of cancer detection and grading. In a prostate biopsy, tissue is removed and assessed for cell abnormalities that may be linked to prostate cancer. The standard grading system for this procedure is the Gleason grade (GG) system, involving classification into 1 of 5 prognostic groups. Expert-level AI algorithms for prostate biopsy grading, like this one from Gooogle Health, have recently been developed to combat interpathologist variability associated with grading. In this diagnostic study, retrospective grading of prostate core needle biopsies was conducted at two medical laboratories in the US between October 2019 and January 2020.


Estimating heterogeneous survival treatment effect in observational data using machine learning

arXiv.org Machine Learning

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of TEH and inform better practice, we carry out a comprehensive simulation study representing a variety of confounded heterogeneous survival treatment effect settings and varying degrees of covariate overlap. Our results indicate that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently carries the best performance, both in terms of bias and precision. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Under lack of overlap, where accurate estimation of the average treatment effect becomes challenging, the credible intervals from AFT-BART-NP still provide nominal frequentist coverage among units near the centroid of the propensity score distribution. Finally, we demonstrate the application of these machine learning methods through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.


Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning

arXiv.org Machine Learning

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.


Israeli Startup Deploys AI-based Solution for Prostate Cancer Detection

#artificialintelligence

This comes shortly after the first implementation of such technology in both France and the UK. Ibex Medical Analytics and CorePlus Servicios Clínicos y Patológicos, LLC, a high complexity CLIA-certified clinical and anatomic pathology laboratory, today announced the first deployment in the Americas of an artificial intelligence (AI) powered digital pathology solution for detecting prostate cancer at its main facilities in Carolina. The deployment includes Whole Slide Imaging (WSI) techniques used to digitize traditional glass slides and the Galen Prostate, an AI-based solution for prostate cancer detection, both validated following strict College of American Pathologists' guidelines. Patients and urologists will now benefit from this technology, which will become the new standard of care for the diagnosis and treatment of prostate cancer. The global rise of cancer patients around the world has led to an increased demand for screening tests, and the number of pathologists has decreased, leading to longer wait times for cancer diagnosis, and higher error and misdiagnosis rates.


Accuracy and stability of solar variable selection comparison under complicated dependence structures

arXiv.org Machine Learning

In this paper we focus on the variable-selection peformance of solar on the empirical data with complicated dependence structures and, hence, severe multicollinearity and grouping effect issues. We choose the prostate cancer data and the Sydney house price data and apply two lasso solvers, elastic net and solar on them (code can be found at \url{https://github.com/isaac2math/}). The results shows that (i) lasso is affected by the grouping effect and randomly drop variables with high correlations, resulting unreliable and uninterpretable results; (ii) elastic net is more robust to grouping effect; however, it completely lose variable-selection sparsity when the dependence structure of the data is complicated; (iii) solar demonstrates its superior robustness to complicated dependence structures and grouping effect, returning variable-selection results with better stability and sparsity. Also, such stability and sparsity make solar a reliable variable pre-estimation filter of a linear dependence structure esimation (linear probablistic graph learning). The linear probablistic graph estimated on the variable selected by solar returns an intuitive, sparse and stable dependence structure.


Using AI to identify the aggressiveness of prostate cancer

#artificialintelligence

These promising results indicate that the deep learning system has the potential to support expert-level diagnoses and expand access to high-quality cancer care. To evaluate if it could improve the accuracy and consistency of prostate cancer diagnoses, this technology needs to be validated as an assistive tool in further clinical studies and on larger and more diverse patient groups. However, we believe that AI-based tools could help pathologists in their work, particularly in situations where specialist expertise is limited. Our research advancements in both prostate and breast cancer were the result of collaborations with the Naval Medical Center San Diego and support from Verily. Our appreciation also goes to several institutions that provided access to de-identified data, and many pathologists who provided advice or reviewed prostate cancer samples.


Google claims its AI system can grade prostate cancer samples with 72% accuracy

#artificialintelligence

In a study published today in the journal JAMA Oncology, Google researchers claim to have developed an AI system that accurately identifies signs of prostate cancer in biopsies. Building on an algorithm that grades large, surgically removed cancerous segments of prostates, they say their system -- which was developed with support from the Naval Medical Center in San Diego and Verily, Alphabet's life sciences division -- works on the smaller samples extracted during the initial part of cancer care to get diagnoses and prognoses. Prostate cancer biopsies are commonly taken to better evaluate tumors' aggressiveness. The Gleason score, a grading system that classifies cancer cells based on how closely they resemble normal prostate gland tissue, is used to detect problematic masses. But determining which of three Gleason patterns a tumor falls into and assigning a grade based on the relative amounts of pattern in the whole sample is a challenging task -- one that relies on subjective visual inspection and experience.