Healthcare has recently benefited greatly from developments in artificial intelligence (AI) technology.
Improved lung cancer detection is one of AI's most revolutionary uses in the medical field.
Lung cancer continues to be one of the top causes of cancer-related deaths worldwide,
and improving survival rates requires early detection. We will examine how high-accuracy AI is altering the landscape of lung cancer detection in this article,
ushering in a new era of precision medicine and better patient outcomes.
In the kingdom of healthcare and technology, few innovations hold as much promise and potential as the fusion of artificial intelligence and mobile phones.
One of the most exciting developments in this synergy is the use of phone-based AI to detect Alzheimer's disease.
Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide, particularly in older age.
It impairs memory, cognition, and behavior, gradually robbing individuals of their ability to function independently. Early diagnosis of Alzheimer's is essential for effective management and intervention.
Until recently, diagnosis relied heavily on clinical assessments,
which often detected the disease only in its advanced stages.
A recent study demonstrated that diabetic retinopathy, diabetic macular edoema, poor blood glucose management, and high lipid levels may all be detected using deep-learning models trained on external pictures of the eye. The diagnosis process and patient outcomes might both be significantly improved by this technology.
How It Works
Retinal fundus photographs can provide valuable information about a patient's systemic health, but they require specialized equipment and trained professionals to capture and analyze the images. In contrast, external photographs of the eye can be taken with common equipment and potentially from home, making this technology more accessible and cost-effective.
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Genomic tools are demonstrating that many human diseases are molecularly heterogeneous and likely to respond differently to molecularly targeted therapeutics.
For many widely used treatments, the number of patients needed to treat (NNT) for each patient who benefits is large indicating that many patients are being exposed to the risks of serious adverse effects although they do not benefit from the drug.
Consequently, more accurately determining the intended use population for new therapeutics is of increased importance. In this paper,
we describe a new paradigm for identifying and internally validating an estimate of the intended use population in randomized phase III clinical trials.
The approach preserves the type I error of the trial and approaches determination of the intended use population as a classification problem,
not a multiple hypothesis testing problems.
The established molecular heterogeneity of human cancers has had profound effects on the design of cancer therapeutics.
Most cancer drugs are today targeted to molecular alterations present in cancer cells.
Tumors of the same primary site, however, often differ with regard to the alterations that they harbor.
Consequently, this heterogeneity has required the development of new paradigms for clinical development.
In this paper we review some clinical trial designs finding active use in co-development of therapeutics and predictive biomarkers to inform their use in oncology.
Identification of treatment selection biomarkers has become very important in cancer drug development.
Adaptive enrichment designs have been developed for situations where a unique treatment selection biomarker is not apparent based on the mechanism of action of the drug. With such designs,
the eligibility rules may be adaptively modified at interim analysis times to exclude patients who are unlikely to benefit from the test treatment.We consider a recently proposed,
particularly flexible approach that permits development of model‐based multifeature predictive classifiers as well as optimized cut‐points for continuous biomarkers.
A single significance test, including all randomized patients, is performed at the end of the trial of the strong null hypothesis that the expected outcome on the test treatment is no better than control for any of the subset populations of patients accrued in the K stages of the clinical trial.
In this paper, we address 2 issues involving inference following an adaptive enrichment design as described above. The first is specification of the intended use population and estimation of treatment effect for that population following rejection of the strong null hypothesis.
The second issue is defining conditions in which rejection of the strong null hypothesis implies rejection of the null hypothesis for the intended use population.
Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics.
We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population.
That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment.
However, due to complexity of the pathway, and/or technical issues with our characterizing assay,
it is often hard to characterize the target population until well into large-scale clinical trials.
This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding.
This paper proposes a framework for group-sequential adaptive enrichment trials.
Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials.
Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial.
However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size,
and characterize the target population at the end of the trial.
This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error.
