XAI for Medicine September - December 2021

                                  

Introduction 

In recent years, there has been a significant increase in the use of machine learning models and artificial intelligence (AI) in the field of medicine. However, as these models become more complex and are integrated into critical decision-making processes, there is a growing need for explainable AI (XAI). In this blog post, we will explore the importance of XAI in medicine and how it can help ensure the safety, reliability, and ethical use of AI in healthcare.

1. Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts

The scientific commentary discusses the challenges of using deep learning (DL) approaches for delineation of the epileptogenic zone (EZ) from invasive EEG* (iEEG). The article refers to a recent study that used DL to classify dichotomously whether a high-frequency oscillation (HFO) was located on a spike, as spike HFOs are considered more epileptogenic than non-spike HFOs. The authors discuss the challenges faced in using DL approaches for EZ delineation and the types of learning in ML. They also highlight that the generalizability of DL approaches for EZ delineation can be questioned as these studies often use small datasets. The article emphasizes that patient-wise cross-validation is the most adequate assessment of a DL model's generalizability for clinical use in different patients. The article also notes that the automatic detection of HFOs in the iEEG can address the tedious work of manually marking these biomarkers, but their specificity as an EZ biomarker may be limited as HFOs can be generated by healthy tissue. Thus, the authors suggest refining epileptic HFOs using DL to address this issue.

*EEG – Electroencephalography

2. The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps

The article discusses the gaps in menstrual tracking apps by analyzing trends in the intended users and functionalities advertised by these technologies. The study found misalignments between descriptions of menstrual trackers and themes developed from the literature, with narrow characterizations of menstruators and designs for limited needs. The study synthesized gaps in the design of menstrual tracking apps and discussed implications for designing around irregular menstrual cycles as the norm, the embodied, leaky experience of menstruation, and the varied biologies, identities, and goals of menstruators. The article also highlights the need for human-centered AI in FemTech to address technical limitations, design choices, and policy concerns related to menstrual trackers. Finally, the article emphasizes the potential for human-centered AI to transform menstrual tracking apps from body management tools into a nurturing and empowering experience.

3. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research 

The article discusses the use of machine learning (ML) in assessing vascular age, which is an important predictor of cardiovascular disease (CVD). It describes the relationship between arterial stiffness and vascular age and the use of biomarkers such as pulse wave velocity (PWV) and central blood pressure (CBP) in assessing vascular age. Two types of ML models are outlined for vascular age assessment: parameter estimation models and risk classification models. The authors provide examples of clinical applications of these models and describe the capabilities of different supervised ML techniques for developing these models. The main highlights are the opportunities presented by ML for enhancing vascular age assessment in research and clinical practice and calls for further research in this field. To develop accurate and clinically relevant ML models for vascular age assessment, suitable datasets are required, and the article provides guidance on methodology, data quality, and reporting. Additionally, benchmark datasets that reflect the target population and arterial stiffness markers should be established. The article presents case studies on the use of ML in assessing vascular age. There are three potential research directions for assessing vascular age: using electronic health record data, the pulse wave as a source of information, and incorporating measures of vascular age into consumer devices. These methods can help identify patients with known risk factors and inform clinical decision-making, but challenges remain in contextualizing measurements and ensuring data quality. 

4. Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer’s disease

The article discusses the application of deep learning methodologies in identifying pseudo-temporal trajectories in transcriptomic space for Alzheimer's disease (AD) progression. Despite numerous scientific advances, no disease-modifying treatments are currently available for AD, and the complicated molecular aetiology driving the disease calls for a broad search for effective therapeutics beyond the conventional amyloid cascade hypothesis. Recent efforts have begun to model AD progression as a continuous trajectory using cross-sectional transcriptomic data, leveraging the methods developed in single-cell genomics and machine learning. The authors leverage multidimensional, well-characterized and high-quality genomic, neuropathological and clinical data from the Accelerating Medicines Project for Alzheimer’s Disease (AMP-AD) programme and apply deep learning framework to identify pseudo-temporal trajectories in transcriptomic space and the underlying gene signatures for AD progression. They observe that the deep learning model trained on the ROSMAP cohort when applied to two independent AMP-AD data sets, the MAYO RNA-seq study cohort and The Mount Sinai Brain Bank (MSBB) study cohort, results in similar trajectories and sample distribution following a generalized pattern, and the estimated severity index values remain strongly correlated with pathological biomarkers and clinical function scores.

5. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure 

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients’ care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems. 

6. An accurate fuzzy rule-based classification systems for heart disease diagnosis 

The article discusses the need for physicians and healthcare providers to better understand clinical decision-making processes and methods, with a focus on the diagnosis of heart disease, a leading cause of death. A novel data-driven method for heart disease diagnosis using fuzzy clustering and linguistic modifiers is proposed to design a fuzzy rule-based classification system.  Some highlights are the importance of balancing interpretability and precision in machine learning models and the use of Explainable Artificial Intelligence (XAI) techniques to explain machine learning models. The article concludes that the proposed model is superior in terms of balancing interpretability and precision, and that XAI techniques, such as rule-based models, can help in understanding the inference process of the system and building confidence in its decision-making. 

7. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis 

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article the focus is on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. The authors gave an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and they briefly discuss Systems Biology in cancer research. They also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models are examined. Finally, there is a summary of the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice. 

8. XAI Feature Detector for Ultrasound Feature Matching 

Feature matching is a crucial component of computer vision that has various applications. With the emergence of Computer-Aided Diagnosis (CAD), the need for feature matching has also emerged in the medical imaging field. In this paper, a novel algorithm using the Explainable Artificial Intelligence (XAI) approach is proposed in order to achieve feature detection for ultrasound images based on the Deep Unfolding Super-resolution Network (USRNET). Based on the experimental results, the presented method shows higher interpretability and robustness than existing traditional feature extraction and matching algorithms. The proposed method provides a new insight for medical image processing, and may achieve better performance in the future with advancements of deep neural networks.

9. Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy 

Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, ‘Epi-AI’, which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Epi-AI demonstrated a superior sensitivity, 91.4%–98.8%, and specificity, 93.1%–98.8%, in Models I–III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I–III ranged from 66.9%–91.3% sensitivity and 60.8%–97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I–III at 73.2%–80.1% and 75.8%–98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.  

              

10. Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

This study discusses the use of deep neural networks (DNNs) optimized through evolutionary algorithms to classify airway-related symptoms based on mechano-acoustic data signals acquired from laboratory-generated and publicly available datasets. The aim is to create an efficient and explainable DNN that can be used for remote, long-term monitoring of airway symptoms using mechano-acoustic sensing wearables. The article found that the optimized DNNs had a low footprint and predicted airway symptoms with 83.7% accuracy on unseen data. An evolutionary algorithm is utilized to find a small and efficient CNN architecture for multi-class classification of airway symptoms using non-speech audio signals. The fitness for each architecture was calculated based on validation accuracy and inference time. The conclusion is that their work represents the first step in developing effective and explainable AI algorithms for long-term remote monitoring of airway symptoms by mechano-acoustic wearables. 

[1] https://tinyurl.com/ycs3u7c8

[2] https://tinyurl.com/7n6cn79f

[3] https://tinyurl.com/2p99wj95

[4] https://tinyurl.com/2mhdk3h4

[5] https://reader.elsevier.com/reader/sd/pii/S1120179721003203?token=BEA95144A8836E1B990ACDC99C18371B3BDE1222D1413D183A2644007951931FEFD61B5E87775B5A4EAF8FF9FF36922F&originRegion=eu-west-1&originCreation=20230312101038

[6] https://reader.elsevier.com/reader/sd/pii/S2468227621003203?token=4E62B5CBE52EA263D73220259718F2ED42A3B9E198B18688DEB9E064C0EA48D91DBAB524E517887606B0E296D4CFC961&originRegion=eu-west-1&originCreation=20230312101044

[7] https://reader.elsevier.com/reader/sd/pii/S2001037021004281?token=5E294CD11401B9CD5FE28ED8A1A2881E2190BCDAB1F269B27241999B0C5D1195570A73D23FFD4A52A95043A04CD2651C&originRegion=eu-west-1&originCreation=20230312101053

[8] https://ieeexplore.ieee.org/document/9629944

[9] https://iopscience.iop.org/article/10.1088/1741-2552/ac2ca0/pdf

[10] https://www.proquest.com/docview/2615378768/430F25AD46584ABEPQ/1

Team members: Meda Ioana, Petrascu Andreea, Pop Paula.