Artificial Intelligence in Neurology

Primary Category

Digital Neurology

P-Category

Secondary Category

S-Category

Authors:

Talha Nazir MD,
Muhammad Mushood Ur Rehman MBBS,
Junaid Kalia MD

Introduction

Stroke has a significant burden on cost and quality of life

Cost: Global Cost is almost $861 Billion which is around 1.21% of global GDP.
Almost 89% of global stroke death and disability combined resides in low income countries.
Quality of Life: Most common cause of Disability

Cost 200000$ per patient per quality of life gained (ref)

AI/ML has the potential to reduce cost by:

Early detection
Early intervention
Intelligent automation
Intelligent Rehabilitation

AI/ML has already been recognized by FDA for De Novo Clearance

AI/ML in Stroke is the first to be reimbursed by CMS via NTAP (New Technology Add-on Payment (NTAP)

Additional, Stroke has a system of care approach

AI/ML can improve care coordination
AI/ML can help in triaging patients and selecting patients for intervention
With a virtual environment, AI/ML can create a real-time environment for EMS providers.

Review the introduction - before continuing to read

AI/ML in Neurovascular disorder

AI/ML is deployed within the continuum of care of neurovascular disorders

As we need to identify each step in the process and use automation and intelligence

The first step in Focal Neurological deficit is to detect cerebral hemorrhage

This is discussed in detail in the chapter

AI/ML in Cerebral Ischemia

Machine Learning is the part of artificial intelligence that collects data, automates the process, and modifies it based on previous experiences.

AI/ML is deployed at various stages during the stroke care

We have divided the applications in the patient journey

Saving one minute for patient of ischemic stroke equals to

Onset to tPA: 1.85 days of extra healthy life
Onset to EVT: 4.2 days of extra healthy life
TICI 2b-3 revac: 4.3 days of extra healthy life
Patient <55 years old with NIH >10: >1 week of extra healthy life

Patient selection (Detection): The convolution neural network (CNN), a subtype of ANN has been used to classify and analyze imaging.

Rule out hemorrhage
ASPECTS score (tissue at risk)
Large vessel occlusion (automated cerebral large vessel detection)
Ischemic Penumbra (automated cerebral perfusion)

AI/ML algorithms can be deployed by

Training a new algorithm
Use a generic algorithm and modify it as per requirement

Hassan et. al., incorporated Viz.ai software and compared transfer time of patients from primary stroke center to comprehensive stroke center.

The usual time from arrival to cath. lab was 117 minutes.
After incorporation of AI software

Time of transfer was reduced by almost 22.5 minutes.
Total length of stay in hospital or neurological ICU was reduced. (ref)

💡

Improved Patient selection for thrombolytics - (Not just endovascular intevention)

EXTEND ClinicalTrials.gov numbers, NCT00887328 and NCT01580839.

A multicenter, randomized placebo-controlled trial showed that with the help of automated perfusion imaging the use of alteplase between 4.5 and 9 hours after stroke results in higher number of patients with no neurological deficit as compared to placebo. (ref)

ECASS4-EXTEND, and EPITHET are other trials to extend the window to use thrmbolytics. (ref)

Automated Diagnosis of Cerebral Ischemia

AI/ML-based algorithm can help in making a diagnosis especially

Hospitals where expert Neuroradiologist is not available
During night shifts
Hospitals with limited resources.

AI/ML can help in

Differentiating ischemia from hemorrhage
Quantification of infarct
Prediction of function loss

Listed in table 2 are some ML-based algorithms for diagnosing ischemic strokes.

💡

TOAST classification divides acute ischemic stroke into five major types. 1. Atherosclerosis of large artery 2. Embolism from heart 3. Occlusion of small vessel 4. Stroke due to known cause 5. Stroke due to undetermined cause

Alberta Stroke CT Score (ASPECTS)

It is a ten points score based on non-contrast CT

Patients with middle cerebral artery stroke
One point is deducted with each region involved (Figure 1)
A score of less than 7 is associated with the worst prognosis

Adjusted for posterior circulation (pc-ASPECT)

Figure 1: ASPECT Score

Rapid ASPECTS/Automated ASPECTS

Calculation of ASPECTS with AI/ML-based algorithms.

Gregory W Albers and his team compared rapid ASPECTS and CT ASPECTS of physicians with Diffusion-Weighted imaging (DWI).

Rapid ASPECTS has less margin of error
Rapid ASPECTS is better in diagnosing at an early stage of stroke.

Noke Guberina and his team compared automated ASPECTS with three clinicians.

Automated ASPECTS sensitivity was 83%
Automated ASPECTS enhance the plausibility of diagnosis

Some of the RAPID ASPECTS algorithms are listed in Table 2

Automated detection of large vessel occlusion (LVO)

AI/ML algorithms can help the stroke patient with LVO

Diagnosis assistance
Saving time
Analyzing eligibility for endovascular therapy

Algorithms available to detect large vessels occlusion are

Convolution neural network (CNN)
Random Forest Model (RF)
CNN model is superior to the RF model

Table 1: Automated Large Vessel Occlusion (LVO) detection

Authors	Population	Intervention	Comparison/Objective	Outcome
Shalini A. Amukotuwa et.al., (ref)	CT scan of patients with ischemic stroke	Automated LVO detection (RAPID - CTA)	Two Neuroradiologists	RAPID -CTA had better sensitivity
Sven P R Luijten et.al., (ref)	Data from the MR CLEAN Registry and PREST	Automated LVO detection	Assessment by an expert neuroradiologist	The algorithm performs better for proximal LVO but varies with location
Zhicai Chen et. al., (ref)	CT Scan of Stroke ischemic patient	Automated LVO detection by an artificial neural network (ANN) in the pre-hospital triage stage	Established pre-hospital prediction scale	The AUC and accuracy of the ANN algorithm were higher than the established prediction scale.

Segmentation

It is important to differentiate between reversible injury, ischemic injury, and infarct.

Automated quantification of ischemic penumbra.

Common methods of segmentation

Relative cerebral blood flow (rCBF)
Diffusion-weighted imaging

Maier and his team reported

RF and CNN models outperformed as compared to all other models
RF and CNN models underperformed as compared to the radiologist

Dice score is the widely used score to compare the efficacy of AI/ML algorithms.

A score is closer to one means higher efficacy

ATLAS (Automated Tree Learning Anomaly Segmentation)

ML algorithm based on decision tree
Used for anterior and middle cerebral artery circulation
The dice score was 0.6122 (108 patient’s data)

Table 2 lists some of the algorithms for automating the segmentation process.

💡

The Decision Tree algorithm is a supervised algorithm that learns from simple decisions and infers from them.

Limitations:

Borderline sensitivity

High false-negative rate

Simple tasks are challenging such as

Artifacts detection
Accounting patients’ motion
Old lesion

Estimation of lesion size

CNN models overestimate for smaller lesions and vice versa

Table 2: AI/ML-based algorithms

Automated ASPECT analysis	Automated Stroke Diagnosis	Automated Segmentation of Infarction
RAPID ASPECT (FDA approved)	Convolution Neural Network	3D CNN
Frontier ASPECTS Prototype	Random Forest	U-Net
iSchema View (FDA approved)	Visual Geometry Group from Oxford (VGG-16),	ATLAS
Random Forest Classifier	Google Net	Generalized Linear Models
Brainomix	Residual Network with 50 layers (ResNet-50)	Random Decision Forests
e-ASPECT	Support Vector Machine	Sparse Autoencoder (SAE) layers
ㅤ	ㅤ	Support Vector Machine (SVM)

Management

The aim of ischemic stroke management is to save the ischemic tissue as much as possible.

There are two modalities that are common in the management of ischemic stroke.

IV tissue plasminogen activator
Emergency endovascular techniques

AI/ML-based algorithm can help in

Selection of patient based on quantified ischemic penumbra.
Outcome prediction
Prediction of complication
Prediction of post-stroke quality of life.

AI/ML can help in triaging patients

Home-based 3D imaging technologies can detect facial asymmetry
The accuracy of video-based applications are up to 87%

Critical timelines for stroke management are

4.5 hours for tPA
Up to 6 hours for endovascular thrombectomy

The delay in first aid provision can lead to permanent functional impairment.

In a pre-hospital context, AI/ML can help in reducing the time from event to first aid.

Using FAST-ED and eFACE assessments.
Automated traffic efficient routes availability for an ambulance.
Telestroke platforms
Automated alerts to the hospital stroke team
Automated registration of patient
Automated allocation of resources

Selection of Patient

It is important to select the treatment modality for the patient as early as possible.

Early treatment selection can help us to avoid unwanted complications.

Inclusion and exclusion of patients are based majorly on the volume of infarction.

The outcome prediction model can help in selecting treatment modalities for patient

ML algorithms can quantify the volume of an infarct with similar accuracy to DWI.

Support Vector Machine (SVM) (AUC of 74%)
3-dimensional pseudo-continuous arterial spin labeling (pCASL) (92% accuracy)
SPOT is an algorithm based on a regression tree model

Helps in the selection of patients for endovascular therapy
Predicts 90 days modified Rankin Scale
AUC was 0.952 (36 patients’ data)

Maarten G Lansberg et.al. did a study on the automated selection of patients for reperfusion therapy (ref)

RAPID algorithm
EPITHET and DEFUSE trial data used
Patient profiles that can respond to tPA can be identified by using RAPID algorithms

Predicting Prognosis

AI/ML algorithm can help in predicting prognosis by analyzing

Clinical data
Imaging
Quantification of ischemic penumbra
Automated perfusion techniques

Predicting prognosis can help in

Improving survival
Devising patient-centered treatment plan
Patient-centered exercises during post-stroke rehabilitation

Predicting Hemorrhagic Conversion

Post thrombolysis/thrombectomy bleed is the most common complication.

AI/ML can help in predicting hemorrhagic conversion

Swarm algorithm
Partial least square(PLS) algorithm
Genetic algorithms.

Chihhsiong and team have developed a modified PSO algorithm based on PLS and genetic algorithm

Helps in predicting hemorrhage based on patient’s co-morbidities
Initial analysis showed an accuracy of 86%

Choi et. al., compared different algorithms’ accuracy in predicting hemorrhagic conversion

Binary Logistic Regression (BLR) - 83.7% accuracy
Support Vector Machine (SVM) - 85.6% accuracy
Extreme Gradient Boosting (XGB) - 83.4% accuracy
Artificial Neural Network crude model (ANN_crude) - 87.8% accuracy

Predicting Malignant Cerebral Edema

Malignant cerebral edema is a life-threatening complication.

Urgent hemi-craniectomy is required because of herniation risk

Foroushani et. al. did a pilot study

Fully Connected and Long short-term memory (LSTM) neural networks were trained
Data used: Clinical and imaging
Compared with regression models and EDEMA score.
The fully connected network didn’t perform well.
LSTM identified all the cases of malignant EDEMA by 24 hours after stroke (fewer false positives)

Predicting Tracheostomy

Post-stroke tracheostomy is a part of continued medical care due to airway compromise.

The identification of the tracheostomy need during the early period of medical care can help in protecting the airway preemptively.

Some of the algorithms available are

Random forest (AUC 0.74)
Gradient Boosting Machine (AUC 0.75)
XGBoost (AUC 0.74)

Predicting post stoke pneumonia

Pneumonia occurring within the first week is usually associated with stroke.

Post-stroke pneumonia is one of the common complications that can increase the length of the stay.

Some of the algorithms that can help in predicting post-stroke pneumonia

Multiple layer perception (MLP)
Recurrent neural networks (RNN)
Logistic regression,
Support vector machines
Extreme gradient boosting

The Attention-augmented gated recurrent unit (GRU) based on RNN performed better both at 7 and 14 days.

Predicting post-stroke cognitive performance

Prediction of post-stroke cognitive function can help in

Patient-centered treatments
Goal-oriented neuro-rehabilitation exercise
Targeted cognitive function
Helps in predicting patient’s quality of life

Chauhan and her team compared four models

Assessed Accuracy and effect of sample size data redundancy.
The hybrid model performed better than others. (Table 3)

Table 3: Algorithms to predict post-stroke cognitive function

Models	Area Under Curve	Prediction of long-term language deficit (R-squared)
Ridge Regression Method (RR) with PCA (principal component analysis)	0.646	0.552
Support Vector Regression (SVR)	0.657	0.592
Convolution Neural Network (CNN)	0.627	0.346
Hybrid Model (RR plus CNN)	0.675	0.550

Extracted from: Chauhan S, Vig L, De Filippo De Grazia M, Corbetta M, Ahmad S and Zorzi M (2019) A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images. Front. Neuroinform. 13:53.

Artificial Intelligence Assisted Neuro-rehabilitation

The global neuro-rehabilitation devices market can reach up to $2.1 billion by 2026 (ref)

Tele-rehabilitation using socially assistive robots can aid in the emotional support and engagement of patients.

Two models of remote rehabilitation

The model that is Home centered
The model that reduces the time of patients in institutions

Limitations of existing neuro-rehabilitation model of care

Rehabilitation tools are not adaptive
Loss of patient’s engagement and interest
Costly
Dependent on human resources
Required clinical settings

mHealth apps and IoT devices can help in resolving these problems.

Adjustable as per patient needs
Increase difficulty in exercises gradually based on results
Monitor progress
Communicate with healthcare providers
Provides real-time quantifiable data
Patient-centered exercise protocol

Some of the mHealth apps and IoT devices are listed in Table 4.

In resource-limited countries, connected rehab centers can be introduced

Limited supervision
Fewer resources will be required
Cost-effective

Limitations:

Most AI/ML-based devices still need human supervision

Provision of technology as per patient’s needs a certain framework

Regulatory hurdles

Data privacy and security

Table 4: Artificial Intelligence-based mHealth applications and health IoT in Neuro-rehabilitation

4A: mHealth Applications in Neuro-rehabilitation

mHealth Apps	Particulars
Clock Yourself	Helps patients to maintain balance. Patients exercise by moving their feet around the clock face.
Flow Free	A game-based app to help patients. Improves speech, finger dexterity, and also the mood of the patient.
Seeing AI	Helps with speech and language. Reads texts aloud and helps patients with dyslexia.
NHS 24 MSK help	A physiotherapy app helps patients to do exercise along with reminders about their appointments.
Dysphagia	Videos depicting the normal swallowing process. Clinicians can use it to educate people.
Via-therapy	Stroke-specific app showing videos about limb rehabilitation. Heps clinicians refine reasoning and rehab techniques.
Hudl Technique	Helps pro athletes refine their technique. Clinicians can ask their patients to make videos to watch later on.

Extracted from: Faheem, F; Kalia, J, mHealth in Neurology - An Introduction. Neurology Pocketbook. Published January 31, 2022. Accessed February 4, 2022. ‌

4B: Health IoT devices in Neuro-rehabilitation

Devices	Particulars
Robotic Devices	Upper limb: Inmotion2, KINARM End-Point Lower Limb: Gait trainer GT1, Lokomat, LOPES system, Ekso, Indego, Rewalk, Twin. Postural stability: Hunova, KINARM Exoskeleton, Armeo Spring,
Non-Invasive Brain Stimulation	Transcranial magnetic stimulation, Transcranial current stimulation
Neural Interfaces	Electroencephalography(EEG) Brain-Computer Interface (BCI), Magnetoencephalography (MEG) BCI, Functional magnetic resonance imaging, Functional near-infrared spectroscopy, Neural prostheses

Table 5: Commercially Available Software Applications For Artificial Intelligence In Stroke

Software	Regulatory Approval	Imaging Modality	Automated ICH Detection	Automated ASPECTS	Automated LVO Detection	Automated Cerebral Perfusion	Cerebral Aneurysm	Care Coordination	Other
aidoc	FDA Approved	CT and CTA	✓	ㅤ	✓	✓	ㅤ	✓	Large Vessels Occlusion and ICH detection Rapid Triage and Communication solutions
Viz.AI	FDA Approved NTAP Reimbursed	CT, CTA, MRI,	✓	✓	✓	✓	✓	✓	Large Vessels Occlusion and ICH detection Communication and Co-ordination solutions
Rapid.AI	FDA Approved NTAP Reimbursed	CT, CTA, MRI	✓	✓	✓	✓	✓	✓	Pulmonary embolism
Icometrics	FDA Approved	CT, CTA, MRI	✓	✓	✓	✓	ㅤ	✓	Images analysis and quantification of disease icompanion - mhealth app for remote monitoring
Arterys	FDA Approved	CT, CTA, MRI	✓	ㅤ	✓	✓	ㅤ	✓	Segmentation and lesion’s volume quantification on FLAIR MRI
Avicenna.AI	FDA Marked	CT, CTA	✓	✓	✓	ㅤ	ㅤ	ㅤ	Diagnostic solution
Brainomix	CE Marked	CT, CTA	✓	✓	✓	✓	ㅤ	✓	Diagnostic and treatment support.
Nico Lab	ㅤ	CT, CTA	✓	✓	✓	✓	ㅤ	✓	Diagnostic and care communication support
MeThinks	ㅤ	CT, CTA	✓	ㅤ	✓	ㅤ	ㅤ	✓	Large vessels occlusion detection
MaxQ	ㅤ	CT	✓	ㅤ	ㅤ	ㅤ	ㅤ	ㅤ	Diagnostic solution for stroke
Olea pulse	ㅤ	CT, CTA, MRI	✓	ㅤ	✓	✓	✓	ㅤ	Segmentation and lesion’s volume quantification Perfusion mapping solutions
Tera Racon	ㅤ	CT, CTA	ㅤ	ㅤ	✓	✓	ㅤ	ㅤ	Automated vessel segmentation and brain perfusion mapping
AutoMIStar	ㅤ	CT, CTA, MRI	ㅤ	ㅤ	✓	✓	ㅤ	ㅤ	Automated processing and visualization Provides perfusion and diffusion data
Qure ai	FDA Marked	CT	✓	ㅤ	ㅤ	ㅤ	ㅤ	✓	Diagnostic and workflow support Quantify and monitor brain tumor volume
Nanox	ㅤ	CT	✓	ㅤ	ㅤ	ㅤ	ㅤ	ㅤ	Rapid triage solution

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Hidden

Artificial Intelligence in Neurology - Cerebral Ischemia

Introduction

AI/ML in Neurovascular disorder

AI/ML in Cerebral Ischemia