A Review of Artificial Intelligence's Neural Networks (Deep Learning) Applications in Medical Diagnosis and Prediction

Djavanshir, G. Reza; Chen, Xinrui; Yang, Wenhao

doi:10.1109/mitp.2021.3073665

Cited by 27 publications

(11 citation statements)

References 8 publications

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“…Although stereotaxic brain biopsy is the gold standard for histological and genetic classification, pathological diagnoses may remain uncertain in 7%–15% of patients 30 . In recent years, owing to the increased capacity of computing power, the wider range of data, and the availability of better models and algorithms, deep learning has developed significantly 31 . Artificial intelligence combined with imaging omics has made remarkable achievements in medical fields, including disease classification, tumor segmentation, and target detection 32 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

et al. 2023

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BackgroundH3K27M mutation status significantly affects the prognosis of patients with diffuse midline gliomas (DMGs), but this tumor presents a high risk of pathological acquisition. We aimed to construct a fully automated model for predicting the H3K27M alteration status of DMGs based on deep learning using whole‐brain MRI.MethodsDMG patients from West China Hospital of Sichuan University (WCHSU; n = 200) and Chengdu Shangjin Nanfu Hospital (CSNH; n = 35) who met the inclusion and exclusion criteria from February 2016 to April 2022 were enrolled as the training and external test sets, respectively. To adapt the model to the human head MRI scene, we use normal human head MR images to pretrain the model. The classification and tumor segmentation tasks are naturally related, so we conducted cotraining for the two tasks to enable information interaction between them and improve the accuracy of the classification task.ResultsThe average classification accuracies of our model on the training and external test sets was 90.5% and 85.1%, respectively. Ablation experiments showed that pretraining and cotraining could improve the prediction accuracy and generalization performance of the model. In the training and external test sets, the average areas under the receiver operating characteristic curve (AUROCs) were 94.18% and 87.64%, and the average areas under the precision‐recall curve (AUPRC) were 93.26% and 85.4%.ConclusionsThe developed model achieved excellent performance in predicting the H3K27M alteration status in DMGs, and its good reproducibility and generalization were verified in the external dataset.

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Section: Discussionmentioning

confidence: 99%

Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

et al. 2023

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“…Accordingly, K facial images passed through six feature maps with shared parameters, and then K streams were merged in the first fully connected layer. The final fully connected layer was connected to a softmax classifier to output the classification of “healthy control” or “SCZ patient.” The softmax classifier, one of the most important operators in deep learning, normalizes various features according to the number of classifications and generates a probability distribution for each classification (Djavanshir et al., 2021). Accordingly, C‐CNN selects the classification with the maximum probability as the output.…”

Section: Methodsmentioning

confidence: 99%

“…The final fully connected layer was connected to a softmax classifier to output the classification of "healthy control" or "SCZ patient." The softmax classifier, one of the most important operators in deep learning, normalizes various features according to the number of classifications and generates a probability distribution for each classification(Djavanshir et al, 2021).Accordingly, C-CNN selects the classification with the maximum probability as the output. For FE-CNN, the input was a single facial image from Tsinghua-FED.…”

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confidence: 99%

Recognizing schizophrenia using facial expressions based on convolutional neural network

Zhang

Wang

et al. 2023

Brain and Behavior

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Objective Facial expressions have been served as clinical symptoms to convey mental conditions in psychiatry. This paper proposes to recognize patients with schizophrenia (SCZ) using their facial images based on deep learning algorithm, and to investigate objective differences in facial expressions between SCZ patients and healthy controls using deep learning algorithm and statistical analyses. Methods The study consists of two parts. The first part recruited 106 SCZ patients and 101 healthy controls, and videotaped their facial expressions through a fixed experimental paradigm. The video data were randomly divided into two sets, one for training a convolutional neural network (CNN) with the classification of “healthy control” or “SCZ patient” as output and the other for evaluating the classification result of the trained CNN. In the second part, all facial images of the recruited participants were put into another CNN separately, which was priorly trained with a facial expression database and will output the most likely facial expressions of the recruited participants. Statistical analyses were performed on the obtained facial expressions to find out the objective differences in facial expressions between the two recruited groups. Results The trained CNN achieved an overall accuracy of 95.18% for classifying “healthy control” or “SCZ patient.” Statistical results on the obtained facial expressions demonstrated that the objective differences between the two recruited groups were statistically significant (p < .05). Conclusions Facial expressions hold great promise as SCZ clues with the help of deep learning algorithm. The proposed approach would be potentially applied to mobile devices for autorecognizing SCZ in the context of clinical and daily life.

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“…For the neural network model, we employed a fully convolutional neural To evaluate the feasibility of this approach, we conducted simulations using blood vessel patterns. For the neural network model, we employed a fully convolutional neural network (FCN) based on a U-net architecture with skip connections [80][81][82][83]. Our training dataset comprised blur-less blood vessel images and blurred images generated using the PSF of Equation ( 6) at varying depths d. By altering the orientation and depth, we generated multiple images from a single original blood vessel image.…”

Section: Software-based Scattering Suppressionmentioning

confidence: 99%

Near-Infrared Transillumination for Macroscopic Functional Imaging of Animal Bodies

Shimizu

2023

Biology

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The classical transillumination technique has been revitalized through recent advancements in optical technology, enhancing its applicability in the realm of biomedical research. With a new perspective on near-axis scattered light, we have harnessed near-infrared (NIR) light to visualize intricate internal light-absorbing structures within animal bodies. By leveraging the principle of differentiation, we have extended the applicability of the Beer–Lambert law even in cases of scattering-dominant media, such as animal body tissues. This approach facilitates the visualization of dynamic physiological changes occurring within animal bodies, thereby enabling noninvasive, real-time imaging of macroscopic functionality in vivo. An important challenge inherent to transillumination imaging lies in the image blur caused by pronounced light scattering within body tissues. By extracting near-axis scattered components from the predominant diffusely scattered light, we have achieved cross-sectional imaging of animal bodies. Furthermore, we have introduced software-based techniques encompassing deconvolution using the point spread function and the application of deep learning principles to counteract the scattering effect. Finally, transillumination imaging has been elevated from two-dimensional to three-dimensional imaging. The effectiveness and applicability of these proposed techniques have been validated through comprehensive simulations and experiments involving human and animal subjects. As demonstrated through these studies, transillumination imaging coupled with emerging technologies offers a promising avenue for future biomedical applications.

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A Review of Artificial Intelligence's Neural Networks (Deep Learning) Applications in Medical Diagnosis and Prediction

Cited by 27 publications

References 8 publications

Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

Deep learning‐based prediction of H3K27M alteration in diffuse midline gliomas based on whole‐brain MRI

Recognizing schizophrenia using facial expressions based on convolutional neural network

Near-Infrared Transillumination for Macroscopic Functional Imaging of Animal Bodies

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