Introduction
Recent studies show that artificial intelligence (AI) applications can perform on par with medical experts on MRI analysis.1 Such applications, to date, tend to oppose the accuracy of AI to the performance of clinicians. For instance, there have been more than 20 000 studies on deep learning (DL) methods for MRI analyses the last decade, which compare the performance of AI to the one of clinicians.2 Recent work suggests that future studies should focus on the comparison of performance between clinicians using AI and their performance without an AI aid.3 The recent global pandemic, however, revealed another urgent need of early disease diagnosis: the ability to make predictions based on a limited number of cases. The AI computer-aided detection (CAD) frameworks, to date, are based on large amounts of data and require high-performance computing (HPC) infrastructures. To address that lacuna, we propose a synergistic approach, in which clinicians and scientists collaborate for faster, cheaper and more accurate detection, relying on small data sets to make accurate-enough predictions. A promising frontier where AI can assist clinicians is Alzheimer’s disease (AD) since the release of promising clinical studies for a new drug have unearthed the need for its early detection. As it can take up to 20 years before patients with AD show any signs of cognitive decline, it can be challenging to diagnose AD in early stages. We, thus, motivate and implement an AI-CAD framework for the early detection of mild cognitive impairment (MCI) and AD to assist clinicians, while the approach can be extended for the diagnosis of other diseases.
AD is caused by an accumulation of β-amyloid (Aβ) plaques, and abnormal amounts of tau proteins in the brain. This results in synapse loss, where the impulse does not reach the neurons, and in loss of structure or function of neurons, including their death, causing memory impairment and other cognitive problems.4 AD has strong impact on the cognitive and physical functioning of patients, resulting in death. Recent developments in slowing AD decline have increased the relevance of its early detection,5 and MCI plays an important role in this. MCI is a syndrome where the patients have greater cognitive decline than normally expected, but it does not necessarily affect their daily lives. Although some patients with MCI remain stable or return to cognitively normal (CN), there is a 10%–15% risk per year of progression to AD.4 Before the aetiology of AD became known, its diagnosis relied on neurocognitive tests. The development of biomarkers improved AD detection. A common method to diagnose AD is hippocampus segmentation, which relates to memory function, and its small volume is an AD biomarker. For a long time, AD diagnosis was done manually by looking at the brain structure and size of the hippocampus on MRI, which requires practice and precision. Prior studies on automated methods for hippocampus segmentation have used DL approaches with promising results.6 Automated hippocampus segmentation for the diagnosis of AD and MCI, however, requires clinicians’ expertise and is sensitive to interrater and intrarater variability.6
Convolutional neural networks (CNN) can become the foundation of an AI-CAD framework for supporting clinicians in the detection of early AD and MCI, since it is a successful approach for image classification. CNN can improve the performance of image classification,7 and they are becoming increasingly popular in MRI analysis. For instance, recent studies show that CNN can work on par with specialists for classifying MRI of patients with skin cancer.1 Similar approaches with three-dimensional (3D) as well as two-dimensional (2D) CNN have also been used for AD detection with promising results. When it comes to the inner mechanics of these approaches, the classification filter of a 3D CNN slides along all the three dimensions of the input image, resulting in 3D feature maps, whereas in a 2D CNN the classification filter slides along only the height and width of the input image. Thus, the latter results in 2D feature maps, which need less parameters, computational power and execution time. Most prior studies have used 3D CNN achieving high accuracy,8 while others obtained similar results with 2D CNN.9 Although previous work on the topic has established that 3D CNN perform better for patch classifications, the results between 2D and 3D approaches for whole image labelling did not differ much.10 A 3D CNN, however, is more computationally expensive, and, due to the high number of parameters, it requires larger data sets for training.11 Concurrently, prior studies have not incorporated a 2D CNN approach for detecting MCI. A summary of prior 2D and 3D CNN applications in the literature is presented in table 1.
We suggest that medical algorithms should not be solely focused on accuracy but should also be evaluated with respect to how they might impact disparities and operationalise fairness in their adoption. Thus, we investigate the extent to which a 2D CNN can detect MCI and early AD.