A SYSTEM AND METHODS FOR ALL KINDS OF CANCER SCREENING

Abstract

Cancer has become one of the most dangerous diseases in recent times, causing deaths around the globe. Early screening and detection of cancer can help healthcare professionals take necessary steps for the treatment and well-being of patients. Identifying the probability of the disease at an early stage can lead to its elimination or mitigation. The United Nations (UN) has been actively working to bring significant changes globally through its Sustainable Development Goals, one of which focuses on “health and well-being for all.” The emergence of AI has opened up new possibilities for improving cancer screening methods. This new invention introduces a comprehensive cancer screening framework using AI-enabled methodologies. The innovative aspect of this system is its use of deep learning models to address the needs of screening various types of cancer. It incorporates multiple imaging modalities to assist healthcare professionals in diagnosing different forms of cancer. The current invention benefits a wide range of stakeholders, including government hospitals, healthcare departments, medical professionals, cancer therapists, researchers, and academic institutions.

Specification

Description:This new invention introduces a comprehensive cancer screening framework using AI-enabled methodologies. The innovative aspect of this system is its use of deep learning models to address the needs of screening various types of cancer. It incorporates multiple imaging modalities to assist healthcare professionals in diagnosing different forms of cancer. It has a a comprehensive AI-enabled methodology for cancer detection, starting with Data Collection, where various cancer datasets are gathered to ensure the representation of different cancer types, thus facilitating robust model training and evaluation. Following this, the data undergoes Pre-processing, a critical phase that involves cleaning and transforming raw data into a suitable format for analysis, enhancing data quality by addressing missing values and ensuring consistency. The processed data is then divided in the Splitting of the Data stage, typically into training, validation, and test sets, which allows the model to learn from one dataset while being evaluated on others to ensure its performance is accurately assessed. Next is Model Selection, where an appropriate AI model is identified for cancer detection. This step includes selecting a suitable pre-trained model, which has been trained on similar tasks or datasets, and then fine-tuning it for the specific cancer detection requirements. The Fine-Tuning process comprises three main steps: first, Loading Pre-trained Weights, where the model is initialized with existing weights to leverage prior knowledge; second, Modifying the Model, which adjusts the architecture to align with the specific cancer detection task, such as altering the output layer to accommodate the number of cancer types; and third, Freezing Layers, which retains learned knowledge by preventing certain layers from being updated during training. After fine-tuning, the model enters the Training phase, where it learns from the training data, followed by Evaluation, wherein its performance is assessed on unseen data to determine how effectively it generalizes to new cancer cases. This structured workflow exemplifies an AI-driven approach to cancer screening, where pre-trained models are adeptly adapted to meet specific needs, ensuring a balance between leveraging existing knowledge and addressing the particular challenges of cancer detection.

BACKGROUND OF THE INVENTION
Cancer has become one of the most dangerous diseases in recent times, causing deaths around the globe. Early screening and detection of cancer can help healthcare professionals take necessary steps for the treatment and well-being of patients. Identifying the probability of the disease at an early stage can lead to its elimination or mitigation. The United Nations (UN) has been actively working to bring significant changes globally through its Sustainable Development Goals, one of which focuses on "health and well-being for all." The emergence of AI has opened up new possibilities for improving cancer screening methods. The following are the relevant patents.

US20210041444A1: The current innovation pertains to the detection of ovarian and colorectal malignancies (CRC and OC, respectively). The interaction between CRC or OC and endogenous small molecules is described in the current invention. The assessment of vitamin E isoforms and associated metabolites is specifically connected to the diagnosis of OC and CRC in the context of the current invention. Additionally related to diagnostic indicators found in the aforementioned procedure is the current invention. The current invention pertains to the pre-symptomatic and underlying case phases of colorectal cancer (CRC), the diagnosis of the disease at different stages and intensities, early identification of CRC, and the tracking and diagnosis of the impact of treatment on CRC and other occult conditions.

US11221340B2: The current application consists of kits, reagents, systems, devices, biomarkers, and procedures for the identification and diagnosis of lung cancer. One element of the program allows for the differential diagnosis of pulmonary nodules as benign or malignant, or it gives biomarkers that may be utilized singly or in different combinations to identify lung cancer. In a different aspect, techniques are offered for diagnosing lung cancer in an individual. These techniques involve identifying, in a biological sample taken from the individual, at least one biomarker value corresponding to at least one biomarker chosen from the set of biomarkers listed in Tables 18, 20, or 21, from which the individual is either classified as having lung cancer or the likelihood of it being diagnosed is calculated.

US10080774B2: The current invention consists of cancer treatment compositions and techniques utilizing a mutant adenovirus that targets cells with a mutant retinoblastoma pathway and carries a polynucleotide encoding a therapeutic polypeptide. The mutated adenovirus can destroy tumor cells while sparing cells with a wild-type retinoblastoma pathway.

US20200392588A1: The prognostic kits, formulations, and methods for prostate cancer are described here. The techniques involve measuring the expression of a prostate-specific marker and PCA3 in a urine sample and establishing a correlation between the PCA3/prostate-specific marker ratio and the subject's risk of prostate cancer mortality and aggressiveness. The process of predicting the prognosis of prostate cancer in a patient sample involves measuring the quantity of a PCA3 mRNA specific to prostate cancer and the quantity of a prostate-specific marker in the sample; calculating the ratio of this quantity of PCA3 mRNA specific to prostate cancer over the quantity of prostate-specific marker;

US20210047694A1: Generally speaking, the current invention relates to a colorectal (CRC) cell atlas that offers therapeutic targets for patients in need of treatment as well as techniques for forecasting cancer patient outcomes. The atlas has the potential to forecast the outcome of immunotherapy, namely adoptive cell transfer and checkpoint blockade treatment. Here, previously unknown tumor gene programs that may be utilized to forecast response and identify potential targets for therapy are disclosed, with the goal of turning a tumor into a responsive phenotype.
The above patents lack the comprehensive approach in facilitating screening of all kinds of cancers using AI enabled methodology.

OBJECTS OF THE INVENTION
1] Therefore, the object of the present invention is to have a system and methods for screening different types of cancers supporting multiple data modalities.

2] Another object of the present invention is to have a strong pre-processing module that improves the training process for cancer screening.

3] Another object of the present invention is a deep learning-based methodology with multiple models to support the screening of different kinds of cancers.

4] Another object of the present invention is a comprehensive methodology for treatment planning and patient engagement.

5] Yet another important object of the present invention is to have a solution to the problem of cancer screening towards early detection and prognosis.

STATEMENT OF THE INVENTION
The present invention, known as "A System and Methods for All Kinds of Cancer Screening," introduces a comprehensive cancer screening framework using AI-enabled methodologies. The innovative aspect of this system is its use of deep learning models to address the needs of screening various types of cancer. It incorporates multiple imaging modalities to assist healthcare professionals in diagnosing different forms of cancer. It has the AI-enabled methodology for model creation in cancer screening, which represents a systematic and intricate process designed to harness the power of advanced neural networks for enhancing the early detection and diagnosis of various cancer types. This methodology begins with a thorough data collection phase, where diverse datasets are gathered from multiple sources, encompassing different cancer types such as breast, colorectal, well-differentiated neuroendocrine tumors (WNG), prostate, and bone cancer. This heterogeneous data includes medical imaging, clinical records, genomic information, and potentially other relevant biological markers. The richness and variety of this data are crucial, as they provide a comprehensive foundation for the subsequent stages of model development. Once the data is collected, the next phase involves the building of sophisticated neural network models. Several advanced architectures are employed in this process, including VGGNet, ResNet, Inception, DenseNet, and EfficientNet. Each of these models has unique characteristics that make them suitable for extracting and learning intricate patterns from the diverse datasets. For example, VGGNet is known for its simplicity and depth, allowing it to capture fine-grained details in images, while ResNet introduces residual connections that help in training very deep networks effectively. Inception models leverage parallel convolutional layers to capture a variety of features simultaneously, and DenseNet connects layers in a way that encourages feature reuse, enhancing learning efficiency. EfficientNet, on the other hand, optimizes network scaling to improve accuracy while maintaining computational efficiency. Following the model-building phase, the methodology incorporates transfer learning. This technique involves taking pre-trained models-originally trained on large and diverse datasets-and fine-tuning them on the specific datasets related to cancer detection. By doing so, these models can adapt the knowledge they have already acquired to the nuances of the new tasks, thereby significantly improving their performance in detecting specific cancer types. Transfer learning not only accelerates the training process but also mitigates the challenges posed by limited labeled data, which is often a constraint in medical imaging tasks.

After fine-tuning, the refined models undergo a rigorous evaluation to assess their performance in terms of accuracy, sensitivity, specificity, and other relevant metrics. This evaluation ensures that the models are reliable and capable of performing well in real-world scenarios. Once the models meet the desired performance standards, they are saved and packaged for deployment, making them ready for practical screening applications. This deployment phase is crucial, as it involves integrating the models into clinical workflows, where they can assist healthcare professionals in making informed decisions regarding patient diagnosis and treatment options. Overall, this comprehensive AI-enabled approach aims to enhance the accuracy and efficiency of cancer screening processes. By leveraging the capabilities of modern neural networks and advanced data processing techniques, the methodology not only facilitates early detection but also improves the potential for personalized treatment plans, ultimately contributing to better patient outcomes in the fight against cancer.

BRIEF DESCRIPTION OF THE DRAWING
This new invention introduces a comprehensive cancer screening framework using AI-enabled methodologies. The innovative aspect of this system is its use of deep learning models to address the needs of screening various types of cancer. It incorporates multiple imaging modalities to assist healthcare professionals in diagnosing different forms of cancer. The current invention is illustrated with the many drawings given below.





Figure 1: Outline of the current invention meant for cancer screening using AI-enabled methodology



Figure 2: AI-enabled methodology for model creation in the current invention meant for cancer screening using AI-enabled methodology















Figure 3: More technical details of the current invention meant for cancer screening using AI-enabled methodology






Figure 4: Illustrates stakeholders for which the invention is beneficial


DETAILED DESCRIPTION OF DRAWINGS
This new invention introduces a comprehensive cancer screening framework using AI-enabled methodologies. The innovative aspect of this system is its use of deep learning models to address the needs of screening various types of cancer. It incorporates multiple imaging modalities to assist healthcare professionals in diagnosing different forms of cancer. The details of the drawings are provided in the preceding section.

Figure 1 illustrates a comprehensive AI-enabled methodology for cancer detection, starting with Data Collection, where various cancer datasets are gathered to ensure the representation of different cancer types, thus facilitating robust model training and evaluation. Following this, the data undergoes Pre-processing, a critical phase that involves cleaning and transforming raw data into a suitable format for analysis, enhancing data quality by addressing missing values and ensuring consistency. The processed data is then divided in the Splitting of the Data stage, typically into training, validation, and test sets, which allows the model to learn from one dataset while being evaluated on others to ensure its performance is accurately assessed. Next is Model Selection, where an appropriate AI model is identified for cancer detection. This step includes selecting a suitable pre-trained model, which has been trained on similar tasks or datasets, and then fine-tuning it for the specific cancer detection requirements. The Fine-Tuning process comprises three main steps: first, Loading Pre-trained Weights, where the model is initialized with existing weights to leverage prior knowledge; second, Modifying the Model, which adjusts the architecture to align with the specific cancer detection task, such as altering the output layer to accommodate the number of cancer types; and third, Freezing Layers, which retains learned knowledge by preventing certain layers from being updated during training. After fine-tuning, the model enters the Training phase, where it learns from the training data, followed by Evaluation, wherein its performance is assessed on unseen data to determine how effectively it generalizes to new cancer cases. This structured workflow exemplifies an AI-driven approach to cancer screening, where pre-trained models are adeptly adapted to meet specific needs, ensuring a balance between leveraging existing knowledge and addressing the particular challenges of cancer detection.

Figure 2 illustrates the AI-enabled methodology for model creation in cancer screening, which represents a systematic and intricate process designed to harness the power of advanced neural networks for enhancing the early detection and diagnosis of various cancer types. This methodology begins with a thorough data collection phase, where diverse datasets are gathered from multiple sources, encompassing different cancer types such as breast, colorectal, well-differentiated neuroendocrine tumors (WNG), prostate, and bone cancer. This heterogeneous data includes medical imaging, clinical records, genomic information, and potentially other relevant biological markers. The richness and variety of this data are crucial, as they provide a comprehensive foundation for the subsequent stages of model development. Once the data is collected, the next phase involves the building of sophisticated neural network models. Several advanced architectures are employed in this process, including VGGNet, ResNet, Inception, DenseNet, and EfficientNet. Each of these models has unique characteristics that make them suitable for extracting and learning intricate patterns from the diverse datasets. For example, VGGNet is known for its simplicity and depth, allowing it to capture fine-grained details in images, while ResNet introduces residual connections that help in training very deep networks effectively. Inception models leverage parallel convolutional layers to capture a variety of features simultaneously, and DenseNet connects layers in a way that encourages feature reuse, enhancing learning efficiency. EfficientNet, on the other hand, optimizes network scaling to improve accuracy while maintaining computational efficiency. Following the model-building phase, the methodology incorporates transfer learning. This technique involves taking pre-trained models-originally trained on large and diverse datasets-and fine-tuning them on the specific datasets related to cancer detection. By doing so, these models can adapt the knowledge they have already acquired to the nuances of the new tasks, thereby significantly improving their performance in detecting specific cancer types. Transfer learning not only accelerates the training process but also mitigates the challenges posed by limited labeled data, which is often a constraint in medical imaging tasks.

After fine-tuning, the refined models undergo a rigorous evaluation to assess their performance in terms of accuracy, sensitivity, specificity, and other relevant metrics. This evaluation ensures that the models are reliable and capable of performing well in real-world scenarios. Once the models meet the desired performance standards, they are saved and packaged for deployment, making them ready for practical screening applications. This deployment phase is crucial, as it involves integrating the models into clinical workflows, where they can assist healthcare professionals in making informed decisions regarding patient diagnosis and treatment options. Overall, this comprehensive AI-enabled approach aims to enhance the accuracy and efficiency of cancer screening processes. By leveraging the capabilities of modern neural networks and advanced data processing techniques, the methodology not only facilitates early detection but also improves the potential for personalized treatment plans, ultimately contributing to better patient outcomes in the fight against cancer.

Figure 3 illustrates the AI-enabled methodology for cancer screening is a comprehensive and patient-centered process that begins with the arrival of a new patient at a healthcare facility. Upon arrival, a doctor performs a series of diagnostic procedures tailored to the patient's specific symptoms and medical history. These procedures may involve a combination of imaging techniques, such as X-rays, MRIs, or CT scans, along with laboratory tests and biopsies to collect relevant data. This initial data collection is critical, as it forms the basis for subsequent cancer detection analysis. Once the data is gathered, it undergoes thorough analysis for potential indicators of cancer. This phase leverages advanced analytical techniques, including statistical methods and machine learning algorithms, to identify any suspicious patterns or abnormalities that may suggest the presence of cancer. If the analysis indicates that cancer is detected, the collected data is then input into an AI model designed specifically for inference in cancer detection. The model processes this data using its trained algorithms, applying complex computations to assess the likelihood of cancer and identify its potential type and stage.

Following the inference, post-processing steps are employed to interpret the model's results accurately. This includes generating detailed reports that summarize the findings and highlight any critical indicators of cancer. These reports are subsequently reviewed by medical professionals, who discuss the results in the context of the patient's overall health and medical history. This collaborative review ensures that the findings are understood comprehensively and that any potential uncertainties are addressed. Based on the interpreted results, the treatment planning stage is initiated. This involves careful consideration and discussion of various treatment strategies, tailored to the patient's specific diagnosis and individual circumstances. Treatment options may range from surgical interventions to chemotherapy, radiation therapy, or a combination of these modalities. The planning process is crucial, as it seeks to optimize the therapeutic approach to enhance patient outcomes.

In instances where no cancer is detected, the process does not simply conclude; instead, it loops back to incorporate further monitoring or adjustments in diagnosis. Continuous monitoring is vital in maintaining patient health, as it allows for timely intervention should new symptoms arise or if there are changes in the patient's condition. This iterative loop ensures that patients receive comprehensive care throughout their journey. Following treatment planning, the methodology incorporates a follow-up stage, where the patient's progress is closely monitored over time. This stage involves regular assessments to evaluate the effectiveness of the treatment and make any necessary adjustments. If the patient experiences changes in their condition or response to treatment, healthcare providers can adapt the treatment strategy accordingly to ensure the best possible outcomes. Parallel to these clinical processes is an essential continuous model improvement phase. In this stage, data from patient outcomes-whether successful or otherwise-is systematically collected and analyzed. This feedback loop serves to update and refine the AI model, enhancing its accuracy and effectiveness in future screenings. By leveraging real-world data and learning from each patient interaction, the AI system becomes increasingly proficient at detecting cancer, ultimately contributing to improved diagnostic accuracy and more effective treatment strategies in subsequent cases. This holistic approach not only emphasizes patient care but also fosters a culture of learning and adaptation within the healthcare system, driving advancements in cancer screening methodologies.

Figure 4 illustrates that the current invention benefits many stakeholders. These include government hospitals, healthcare departments, medical professionals, cancer therapists, researchers, and academic institutions.


, Claims:I Claim
1. A system and methods for screening different types of cancers supporting multiple data modalities.

2. A solid pre-processing module that improves the training process for cancer screening.

3. A deep learning-based methodology with multiple models to support screening of different kinds of cancers.

4. A comprehensive methodology for treatment planning and patient engagement.

5. A solution to the problem of cancer screening towards early detection and prognosis.

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