As the number of dementia patients rises, the need for accurate diagnostic
procedures rises as well. Current methods, like using an MRI scan, rely on
human input, which can be inaccurate. However, the decision logic behind
machine learning algorithms and their outputs cannot be explained, as most
operate in black-box models. Therefore, to increase the accuracy of diagnosing
dementia through MRIs, a convolution neural network has been developed and
trained using an open-source database of 6400 MRI scans divided into 4 dementia
classes. The model, which attained a 98 percent validation accuracy, was shown
to be well fit and able to generalize to new data. Furthermore, to aid in the
visualization of the model output, an explainable AI algorithm was developed by
visualizing the outputs of individual filters in each convolution layer, which
highlighted regions of interest in the scan. These outputs do a great job of
identifying the image features that contribute most to the model
classification, thus allowing users to visualize and understand the results.
Altogether, this combination of the convolution neural network and explainable
AI algorithm creates a system that can be used in the medical field to not only
aid in the proper classification of dementia but also allow everyone involved
to visualize and understand the results.

研发了一个使用卷积神经网络和可解释 AI 算法的系统，可用于精确诊断痴呆症，并让所有相关人员可视化和理解结果。

基于卷积神经网络和可解释人工智能的脑部 MRI 扫描诊断痴呆症

Using a Convolutional Neural Network and Explainable AI to Diagnose  Dementia Based on MRI Scans

Objectives: The present study evaluated the impact of a commercially
available explainable AI algorithm in augmenting the ability of clinicians to
identify lung cancer on chest X-rays (CXR).
Design: This retrospective study evaluated the performance of 11 clinicians
for detecting lung cancer from chest radiographs, with and without assistance
from a commercially available AI algorithm (red dot, this http URL) that predicts
suspected lung cancer from CXRs. Clinician performance was evaluated against
clinically confirmed diagnoses.
Setting: The study analysed anonymised patient data from an NHS hospital; the
dataset consisted of 400 chest radiographs from adult patients (18 years and
above) who had a CXR performed in 2020, with corresponding clinical text
reports.
Participants: A panel of readers consisting of 11 clinicians (consultant
radiologists, radiologist trainees and reporting radiographers) participated in
this study.
Main outcome measures: Overall accuracy, sensitivity, specificity and
precision for detecting lung cancer on CXRs by clinicians, with and without AI
input. Agreement rates between clinicians and performance standard deviation
were also evaluated, with and without AI input.
Results: The use of the AI algorithm by clinicians led to an improved overall
performance for lung tumour detection, achieving an overall increase of 17.4%
of lung cancers being identified on CXRs which would have otherwise been
missed, an overall increase in detection of smaller tumours, a 24% and 13%
increased detection of stage 1 and stage 2 lung cancers respectively, and
standardisation of clinician performance.
Conclusions: This study showed great promise in the clinical utility of AI
algorithms in improving early lung cancer diagnosis and promoting health equity
through overall improvement in reader performances, without impacting
downstream imaging resources.

本研究评估了商业可用的可解释的 AI 算法在增强临床医生识别胸部 X 光片上的肺癌的能力方面的影响。研究结果表明，使用 AI 算法可以提高肺癌的检测性能，减少漏诊，促进健康公平。