Description:FIELD OF THE INVENTION
This invention relates to Smart Diagnostic Assistant for ALS Detection A Feasible AI Model for Clinical Deployment and Decision Support
BACKGROUND OF THE INVENTION
Amyotrophic Lateral Sclerosis (ALS) is a progressive, chronic neurodegenerative illness, which is notoriously difficult to diagnose since its symptoms bear a similarity to other neurological illnesses. Symptom onset to confirmed diagnosis answer time is more than 12 months on average, with conceivably a very lengthy clinical delay prior to patient care being planned and treated. Current diagnostic approaches are heavily dependent on clinician expertise and subjective judgment of symptoms, leading to resulting inconsistency and uncertainty. Further delay and misdiagnosis occur due to poor access to advanced diagnostic equipment in most clinical environments. Furthermore, the majority of AI models employed in neurology are generic or explainability-impaired and thus non-deployable in real-world situations. Such obstacles are addressed by this invention through the offering of an explainable AI-based decision support system trained solely for ALS diagnosis. By EMG input data, clinical history, and neurological test inputs, the model provides the opportunity for the clinicians to provide rapid and accurate decisions. Its integration into healthcare infrastructure ensures that it is a part of the clinical process and, hence, will complement rural and urban healthcare infrastructure. The solution will fill the diagnostics gap and reduce the dependency on expert timetabling, hence resulting in faster and more courageous clinical decision-making.
PRIOR ART
US20240404685: This application describes, among other things, methods of selecting a task-specific machine-learning model for addressing a clinical task. An example method includes receiving a prompt from a user. Based on determining that the prompt requests assistance with a clinical task, a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks selects a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt. The prompt is provided to the selected task-specific machine-learning model. And a response received from the selected task-specific machine-learning model is provided to the user.
US20240404702: This application describes, among other things, machine-learning models for performing specific clinical tasks. An example method includes receiving a prompt at a first computing system in communication with a machine-learning model trained to assist in performing a clinic task that includes generating a report of a patient's medical records, guiding a patient through a care plan, creating patient care guidelines based on a patient's health profile, identifying patients requiring follow-up at a hospital, identifying changes in a standard of care for a disease setting, or evaluating unstructured data associated with a patient to identify a cohort of similar patients. Based on the prompt, a natural language response is generated that is responsive to the prompt and is based on an analysis by the machine-learning model of a repository of data that is determined to be relevant to the prompt. And the natural language response is provided to second computing system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention relates to a Smart Diagnostic Assistant powered by artificial intelligence (AI) to assist clinicians in the early detection and management of Amyotrophic Lateral Sclerosis (ALS). The system integrates multimodal patient data including electromyography (EMG) signals, neurological test results, symptoms, and electronic health records (EHRs). Using deep learning combined with explainable AI (XAI) mechanisms, it produces interpretable outputs in the form of visualizations and confidence scores. Unlike conventional diagnostic approaches or black-box AI models, the invention improves diagnostic accuracy, reduces delays, and ensures clinician trust through transparent decision support. The system is deployable in hospital information systems, telemedicine platforms, and rural clinics, with modular scalability for future neurodegenerative diseases.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The on-sale product is a Smart Diagnostic Assistant based on artificial intelligence to assist clinicians with early diagnosis of Amyotrophic Lateral Sclerosis (ALS). The software integrates multimodal patient data such as EMG signals, symptoms, and neurological tests, utilizing deep learning models to identify patterns of ALS. The software offers explainable predictions for clinician confidence and insight enrichment. The system has a deployable prototype interface into existing hospital information systems. Automated alert, real-time decision support, and simple-to-interpret dashboards improve diagnostic speed and accuracy. The invention ultimately aims to minimize diagnostic delay, maximize clinical transparency, and maximize individualized care in ALS patients through scalable deployment into healthcare systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The present invention relates to a Smart Diagnostic Assistant System for early detection of Amyotrophic Lateral Sclerosis (ALS), and more particularly to an artificial intelligence–based framework that integrates multimodal biomedical data, processes it using deep learning techniques, and provides explainable diagnostic support to clinicians in real-world healthcare settings.
ALS is a progressive neurodegenerative disease that is difficult to diagnose in its early stages due to overlapping symptoms with other neurological disorders. Current diagnostic processes depend heavily on clinical expertise and manual interpretation of electromyography (EMG) readings, symptoms, and neurological test results. This results in significant diagnostic delays, often exceeding one year from symptom onset to confirmed diagnosis. The present invention addresses this problem by providing a unified system and method capable of analyzing multimodal data and producing transparent diagnostic outputs in real time.
The invention comprises a data acquisition module designed to receive multimodal patient information including EMG signals, patient-reported symptoms, results from neurological tests, and electronic health records (EHR). In certain embodiments, this module may be integrated with EMG-enabled wearable devices to facilitate real-time monitoring of patient muscle activity.
A preprocessing unit is configured to filter, normalize, and clean the acquired data. Noise and outliers in EMG signals are removed, and patient records are harmonized into standardized formats to ensure consistent analysis. This preprocessing step improves diagnostic reliability and ensures that subsequent model inputs are clinically relevant.
The invention incorporates a deep learning framework comprising convolutional neural networks (CNNs) for processing EMG signals and recurrent neural networks (RNNs) or transformer-based models for interpreting sequential clinical data. This framework is trained on ALS-specific datasets to identify early disease progression indicators, thereby enabling early detection that would otherwise be missed in traditional diagnostic approaches.
An explainable AI (XAI) module is integrated within the framework to provide interpretable outputs. Unlike black-box AI systems, the XAI module generates feature importance graphs, attention maps, or visual heatmaps that highlight which data inputs most influenced the prediction. This enhances transparency and builds clinician trust in the system.
The invention further comprises a clinical integration interface for seamless incorporation into hospital information systems (HIS) and electronic health records (EHR) through HL7 and FHIR protocols. This ensures that the system works in harmony with existing hospital workflows and does not require additional infrastructure.
A reporting and alert unit is configured to generate diagnostic reports and notify clinicians when high-risk ALS cases are detected. Notifications may be transmitted via hospital dashboards, email, or SMS to ensure timely referral and treatment.
A data security module ensures that patient information remains secure at all stages of acquisition, processing, and reporting. Medical data is encrypted using AES-256 standards, and the architecture is designed to comply with GDPR and HIPAA regulations, making the invention globally deployable.
The system is highly versatile due to its deployment layer, which enables flexible installation across different environments. It may be deployed on hospital servers for large-scale clinical use, implemented as a cloud-based service for telemedicine networks, or installed as an edge AI device for offline diagnosis in rural healthcare facilities with limited connectivity.
In one embodiment, the invention also includes a voice-based clinician interaction interface that allows healthcare professionals to input symptoms or interact with diagnostic results using voice commands. This provides hands-free operation and improves usability in time-constrained clinical environments.
The corresponding method of operation begins with the acquisition of multimodal patient data, followed by preprocessing to remove noise and standardize the input. Features are then extracted using the deep learning framework, and diagnostic probabilities are computed. The explainable AI module generates transparent diagnostic outputs, which are integrated into hospital systems and made available on clinician dashboards. Reports and alerts are issued automatically for high-risk cases. The method concludes with secure storage and transmission of results, ensuring compliance with medical data protection standards.
The invention provides significant advantages over prior solutions. It reduces diagnostic delay, improves treatment planning, and enhances clinician confidence through explainability. Its modular and scalable design enables deployment across diverse healthcare environments, from advanced urban hospitals to low-resource rural clinics. By integrating multimodal data and explainable outputs, the invention establishes a practical and reliable tool for ALS detection and clinical decision support.
The on-sale product is a Smart Diagnostic Assistant based on artificial intelligence to assist clinicians with early diagnosis of Amyotrophic Lateral Sclerosis (ALS). The software integrates multimodal patient data such as EMG signals, symptoms, and neurological tests, utilizing deep learning models to identify patterns of ALS. The software offers explainable predictions for clinician confidence and insight enrichment. The system has a deployable prototype interface into existing hospital information systems. Automated alert, real-time decision support, and simple-to-interpret dashboards improve diagnostic speed and accuracy. The invention ultimately aims to minimize diagnostic delay, maximize clinical transparency, and maximize individualized care in ALS patients through scalable deployment into healthcare systems.
Key Components and Technology
The Smart Diagnostic Assistant takes advantage of hardware and software technologies for the benefit of facilitating transparent, early ALS detection.
1. Data Sources:
• EMG signals
• Clinical symptom inputs (e.g., speech impairment, muscle weakness)
• Neurological exam and patient history
• Electronic Health Records (EHR)
2. Machine Learning Framework:
• Hybrid deep learning framework consisting of Convolutional Neural Networks (CNNs) for signal processing and Recurrent Neural Networks (RNNs) or Transformers for sequence prediction.
• Explainability Layer employing SHAP (SHapley Additive Explanations) or attention mechanism to produce human-understandable explanation.
3. Software Framework:
• TensorFlow/PyTorch with Python for model training and prediction generation.
• Flask/Django backend for interaction with API.
• Front-end interface using React for clinician dashboard.
4. Clinical Integration:
• HL7/FHIR protocols for secure integration with hospital systems.
• API-based integration with majority of HIS or EHR systems.
5. Data Security & Privacy:
• AES-256 encryption of stored medical data.
• GDPR and HIPAA-compliant architecture ready for global deployment.
6. Power & Hosting:
• On-premises deployment or cloud hosting on security-featured health-cloud providers (e.g., AWS HealthLake, Google Cloud Healthcare API).
• Support for edge AI enablement for offline diagnosis in remote regions.
This technology stack is specifically architected to support accurate detection, clinician confidence, clinical usability, and healthcare regulatory compliance.
Six Stepwise Working Functionality
1. Patient Data Collection
It receives input data from clinical tests, EMG recordings, and previous medical histories entered manually or pulled from hospital databases.
2. Preprocessing & Normalization
Raw EMG and clinical data are filtered, normalized, and cleaned by medical data preprocessing techniques. Noise and outliers are removed for accuracy.
3. Feature Extraction
Deep layers learn informative patterns from EMG signals and clinical information. NLP units identify text-based health records, and CNN units identify waveform abnormalities.
4. ALS Probability Prediction
The joint model generates the probability of ALS onset from learned features and trained classification models. Predictions are ranked according to confidence scores.
5. Explainable Diagnosis Support
The model generates high-value visualizations (e.g., heatmaps, SHAP values) of which features were to blame for prediction of diagnosis in aid of clinician decision-making.
6.Alert & Reporting System
It notifies doctors either through HIS or e-mail/SMS upon recognition of high-risk cases. It generates patient-specific results report with confidence level and suggested next step (e.g., more testing or referral to neurologist).
This is to allow the model to work within actual health care settings with promises of speed efficiency, transparency, and support in diagnosis.
ADVANTAGES OF THE INVENTION
1. Public Health Promotion with early diagnosis and treatment of ALS in healthcare.
2. Rural-Urban Healthcare Disparities eradicated due to cloud-supported diagnosis and telemedicine.
3. Enabling AI in Medical Practice, leading Indian digital health innovation.
4. Evades Cost Burden through not imposing cost burden of stage-delayed diagnosis and treatment.
5. Enabling Data-Driven Research through aggregating and consolidating Indian ALS statistics in order to help improve healthcare.
The novelty of this invention is the use of explainable AI (XAI), multimodal biomedical data fusion, and real-time clinical system compatibility for ALS detection. Unlike standard diagnostic tools, the assistant is trained on ALS-specific information, for example, identifying early-stage signs and progression indicators. The interpretable deep learning architectures are visual reasoning such as heatmaps, attention layers to offer more transparency. Another new feature is that it can be executed on Electronic Health Records (EHRs) to facilitate easy clinical deployment. In addition, the modularity of the model facilitates updating new patient data and immune to data bias. The combination of interpretability, disease specificity, and real-world usability is the primary novelty, a new first-of-a-kind methodology in ALS.
, Claims:1. A smart diagnostic assistant system for early detection of Amyotrophic Lateral Sclerosis (ALS), comprising:
a) a data acquisition module configured to receive multimodal patient data including electromyography signals, symptoms, neurological test results, and electronic health records;
b) a preprocessing unit configured to filter, normalize, and clean the multimodal data;
c) a deep learning framework comprising convolutional neural networks for electromyography signal analysis and recurrent neural networks or transformer-based models for sequential clinical data interpretation;
d) an explainable AI module configured to generate interpretable diagnostic outputs including feature importance graphs, attention maps, or heatmaps;
e) a clinical integration interface configured to connect with hospital information systems through HL7 and FHIR protocols;
f) a reporting and alert unit configured to notify clinicians and generate diagnostic reports for high-risk ALS cases; and
g) a data security module ensuring compliance with GDPR and HIPAA standards using AES-256 encryption.
2. A method for early detection of Amyotrophic Lateral Sclerosis (ALS) using the system as claimed in claim 1, comprising the steps of:
a) acquiring multimodal patient data including electromyography signals, symptoms, neurological test results, and electronic health records;
b) preprocessing the multimodal data to remove noise and standardize formats;
c) extracting disease-specific features through the deep learning framework;
d) computing ALS diagnostic probabilities;
e) generating interpretable diagnostic outputs using the explainable AI module; and
f) transmitting alerts and diagnostic reports to clinicians via the clinical integration interface.
3. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the data acquisition module integrates with EMG-enabled wearable devices for real-time monitoring of patient muscle activity.
4. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the preprocessing unit removes artifacts and harmonizes clinical records into standardized formats for improved analysis.
5. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the deep learning framework is trained on ALS-specific datasets to detect early-stage disease progression indicators with high accuracy.
6. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the explainable AI module employs SHAP-based explanations, attention layers, or heatmap visualizations to improve clinician confidence.
7. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the clinical integration interface incorporates diagnostic results directly into patient dashboards of hospital information systems.
8. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the reporting and alert unit sends notifications via hospital dashboards, emails, or SMS messages for timely referral and treatment.
9. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the deployment is adaptable across cloud-based platforms, hospital servers, and edge AI devices to suit both urban and rural healthcare environments.
10. The system as claimed in claim 1 or the method as claimed in claim 2, wherein a voice-based clinician interaction interface enables voice input of patient symptoms and voice-output diagnostic assistance.