scholarly journals Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variables

2022 ◽  
Author(s):  
Lucas M. Thimoteo ◽  
Marley M. Vellasco ◽  
Jorge Amaral ◽  
Karla Figueiredo ◽  
Cátia Lie Yokoyama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Blood Test ◽  
Explainable Artificial Intelligence

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence

Oncology ◽  
2021 ◽  
Vol 99 (5) ◽  
pp. 318-326
Author(s):  
Yutaro Kamei ◽  
Tetsuro Takayama ◽  
Toshiyuki Suzuki ◽  
Kenichi Furihata ◽  
Megumi Otsuki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colon Cancer ◽  
Serum Albumin ◽  
Test Data ◽  
Predictive Factors ◽  
Blood Test ◽  
Stage Iii ◽  
Clinicopathological Factors ◽  
Stage Iii Colon Cancer ◽  
Staging Systems

Background: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopathological factors and blood test data have been used. Various predictive factors for recurrence have been advocated; however, the interactions are complex and remain to be established. We used artificial intelligence (AI) to examine predictive factors related to recurrence. Methods: The study group comprised 217 patients who underwent curative surgery for stage III colon cancer. Using a self-organizing map (SOM), an AI-based method, patients with only 23 clinicopathological factors, patients with 23 clinicopathological factors and 34 of preoperative blood test data (pre-data), and those with 23 clinicopathological factors and 31 of postoperative blood test data (post-data) were classified into several clusters with various rates of recurrence. Results: When only clinicopathological factors were used, the percentage of T4b disease, the percentage of N2 disease, and the number of metastatic lymph nodes were significantly higher in a cluster with a higher rate of recurrence. When clinicopathological factors and pre-data were used, three described pathological factors and the serum C-reactive protein (CRP) levels were significantly higher and the serum total protein (TP) levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. When clinicopathological factors and post-data were used, three described pathological factors, serum CRP levels, and serum carcinoembryonic antigen levels were significantly higher and serum TP levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. Conclusions: This AI-based analysis extracted several risk factors for recurrence from more than 50 pathological and blood test factors before and after surgery separately. This analysis may predict the risk of recurrence of a new patient by confirming which clusters this patient belongs to.

An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

2021 ◽  
pp. 155005942110636
Author(s):  
Francesco Carlo Morabito ◽  
Cosimo Ieracitano ◽  
Nadia Mammone
Keyword(s):  
Artificial Intelligence ◽  
Frontal Lobe ◽  
Parietal Lobe ◽  
Classification Performance ◽  
Head Region ◽  
Left Frontal Lobe ◽  
Performance Accuracy ◽  
Power Spectral ◽  
Explainable Artificial Intelligence

An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients resulted converted to Alzheimer’s Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as “T0” (MCI state) or “T1” (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68–99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure allowed to detect which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) resulted more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.

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