Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors. "We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data. Pathology practice is in the midst of a digital revolution. Computer-based methods are being developed to assist the expert pathologist. Also, in places where there is no expert, such methods and technologies can directly assist diagnosis," explains corresponding author Vijaya B. Kolachalama, PhD, FAHA, assistant professor of medicine and computer science at BUSM.
First of all, why should we bother studying causation? Hasn't machine learning made the study of causation pointless? Many times, we are interested in a relationship of this kind: "if I do X, then Y will happen", which is not the same as "if X happens, then Y will happen". The difference is that in the first case, we're actually forcing X to happen, while in the second case, X is happening spontaneously. This might seem frivolous, but it's rather important.
Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?
Incorporating ventilation images into radiotherapy plans to treat lung cancer could reduce the incidence of debilitating radiation-induced lung injuries, such as radiation pneumonitis and radiation fibrosis. Specifically, ventilation imaging can be used to adapt radiation treatment plans to reduce the dose to high-functioning lung. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) scans are the gold standard of ventilation imaging. However, these modalities are not always readily available and the cost of such exams may be prohibitive. As such, researchers are investigating the feasibility of alternatives such as MR or CT ventilation imaging.
Cancer patients and their doctors have more information about the disease and its treatment than ever before, and the information available continues to grow at a dizzying rate. Think about a lung cancer patient, for instance, who might receive an early diagnosis through a screening program that produces a computed tomography (CT) image. As their diagnosis and treatment plan advances, their caretakers will bring in data sources like MR and molecular imaging, pathology data -- which is increasingly digitized -- and genomics information. "All of this, honestly, is a very difficult challenge for the care teams themselves as they're thinking about how to best care for and treat these patients," Louis Culot, GM of genomics and oncology informatics at Philips, said during an Amazon Web Services virtual event for the health industry. "In oncology now, or in any any medical discipline, this matters because the treatment matters, the intervention matters," Culot said.
Digital pathology is an emerging field which deals with mainly microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them. Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors. We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data.
As artificial intelligence and machine learning technologies continue to be developed, they may become powerful tools in many fields, including that of medicine. AI, complementing human experience and judgement, has already shown promise as a prognostic tool. Recent research using an AI program to help identify, from the results of chest scans, the risk of lung cancer is an example of the technique in action. Lung cancer is the second most common form of cancer worldwide, according to the World Cancer Research Fund. In Australia, it is the leading cause of cancer deaths and Cancer Australia estimates lung cancer accounted for 17.7% of all deaths from cancer in 2021.
Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.
Israeli startup OncoHost announced today an upsized and oversubscribed $35 million Series C funding round, led by ALIVE Israel HealthTech VC, with the participation of Leumi Partners, Menora Mivtachim, OurCrowd and other existing investors. Clinical trial results have shown OncoHost's AI-powered precision oncology platform to have remarkably high accuracy in assessing non-small cell lung cancer (NSCLC) patient response at three months, six months and one year. Through one blood test pre-treatment, the company's multi-patented platform also provides clinicians with potential combination strategies to overcome treatment resistance. Last year, OncoHost CEO Dr. Ofer Sharon told me that "For immunotherapy, the most important treatment modality we have today, the response rate on average across all cancer types is about 20%. With all the promise of immunotherapy, if you have ten patients waiting in your waiting room with advanced cancer, only two will be alive in two years."
Doctors and scientists have developed an artificial intelligence tool that can accurately predict how likely tumours are to grow back in cancer patients after they have undergone treatment. The breakthrough, described as "exciting" by clinical oncologists, could revolutionise the surveillance of patients. While treatment advances in recent years have boosted survival chances, there remains a risk that the disease might come back. Monitoring patients after treatment is vital to ensuring any cancer recurrence is acted on urgently. Currently, however, doctors tend to have to rely on traditional methods, including ones focused on the original amount and spread of cancer, to predict how a patient might fare in future.