Method and device for guiding an operator in performing diagnostic procedures on a patient's body

Abstract

A diagnostic device (10) for guiding a doctor in performing diagnostic procedures on a patient's body comprises a main body (12) having a top surface (13) and a bottom surface (14), with one or more user output modules (22) positioned on the top surface (13) and a set of sensors comprising peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14). A processor (302) executes instructions stored in a memory (306) for receiving and processing preliminary inputs about the patient through a preliminary data acquisition module (307), determining a target body region and starting point, and performing scans to determine boundaries and examine specific organs. The device employs an artificial intelligence / machine learning (AI / ML) engine (310) to generate real-time audio, visual, and haptic instructions for guiding the operator, process multiple data streams, identify abnormalities, and provide diagnostic recommendations.

Description

METHOD AND DEVICE FOR GUIDING AN OPERATOR IN PERFORMING DIAGNOSTIC PROCEDURES ON A PATIENT'S BODYCROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[0001] The present application claims priority from Indian Patent Application No. 202421053172, filed on July 11, 2024.TECHNICAL FIELD

[0002] The present invention, in general, relates to the field of medical diagnostic devices and more particularly, relates to devices and methods for performing diagnostic procedures on patient’s body.BACKGROUND

[0003] The field of medical diagnostics has witnessed significant advancements in recent years, yet substantial challenges persist in providing comprehensive, accurate, and efficient diagnostic solutions, particularly in primary healthcare settings. The increasing complexity of medical conditions, combined with the growing need for rapid and accurate diagnoses, has created a pressing demand for innovative diagnostic tools that can assist healthcare practitioners in their clinical decision-making processes.Current Challenges in Medical Diagnostics:

[0004] Healthcare practitioners, particularly general practitioners (GPs) and specialists, face numerous challenges in their daily practice. A primary challenge is information overload, as the exponential growth of medical knowledge makes it increasingly difficult for practitioners to stay current with the latest diagnostic protocols, treatment options, and medical research. This complexity is further amplified by the vast array of potential diagnoses for common symptoms. Resource limitations present another significant hurdle, as many healthcare facilities, especially in primary care settings, lack access to comprehensive diagnostic tools and specialists. This limitation often results in delayed diagnoses or unnecessary referrals, impacting both healthcare costs and patient outcomes.

[0005] Diagnostic accuracy represents another critical challenge, as the traditional approach to diagnosis, heavily reliant on the practitioner's experience and memory, can be prone to cognitive biases and knowledge gaps. This becomes particularly critical when dealing with complex or rare conditions that require careful consideration of multiple factors. Integration challenges further complicate the diagnostic process, as current medical devices often operate in isolation, making it difficult to integrate and analyze data from multiple sources effectively. This fragmentation can lead to incomplete assessments and potentially missed diagnostic opportunities. Additionally, communication barriers between primarycare physicians and specialists, or between healthcare facilities, often lack standardization and efficiency, potentially compromising patient care continuity.Existing Solutions and Their Limitations:

[0006] Current diagnostic solutions in the medical field can be broadly categorized into several groups, each with its own limitations. Traditional diagnostic devices, such as stethoscopes, blood pressure monitors, and basic imaging equipment, while fundamental to medical practice, operate as standalone tools. They require significant expertise for proper use and interpretation, and their effectiveness is highly dependent on the practitioner's skill level. These tools also lack the ability to integrate their findings with other diagnostic data or provide guidance on their optimal use.

[0007] Modern imaging systems, including MRI, CT, and ultrasound, provide detailed diagnostic information but face significant limitations. These systems are typically expensive and require specialized facilities, making them inaccessible in many primary care settings. They are often limited to specific types of examinations and require extensive training for operation and interpretation. Furthermore, they are unable to provide real-time guidance or decision support during the diagnostic process.

[0008] Clinical Decision Support Systems (CDSS), while valuable, often suffer from limited integration with physical examination processes and rely heavily on manual data entry. They typically lack real-time feedback during patient examination and have insufficient incorporation of physical examination findings. Their ability to adapt to individual patient characteristics remains limited, reducing their effectiveness in personalized medicine approaches.Need for Innovation:

[0009] The current healthcare landscape demands a more integrated and intelligent approach to medical diagnostics, driven by several key factors. Rising healthcare costs necessitate more efficient diagnostic processes that can reduce unnecessary referrals, minimize redundant tests, enable more accurate initial diagnoses, and optimize resource utilization in healthcare settings.

[0010] The growing complexity of medical care presents additional challenges, as healthcare providers face aging populations with multiple comorbidities, an expanding understanding of disease mechanisms, and an increasing array of treatment options. The trend toward specialization in medical practice, combined with the need for personalized medicine approaches, further amplifies this complexity.

[0011] The ongoing digital transformation of healthcare creates opportunities for significant improvements in diagnostic capabilities. This transformation enables theintegration of multiple data sources, real-time analysis and decision support, improved communication between healthcare providers, enhanced documentation and recordkeeping, and better patient engagement and education. The emergence of artificial intelligence and machine learning in healthcare opens new possibilities for pattern recognition in diagnostic data, real-time guidance during examinations, integration of multiple diagnostic modalities, and continuous learning from clinical experiences.

[0012] The current healthcare landscape faces significant challenges in integrating various medical disciplines and treatment approaches. While modem medicine has advanced considerably, there remains a notable gap in systems that can effectively integrate and suggest treatment options across different medical practices including allopathy, ayurveda, homeopathy, and traditional home remedies. This fragmentation often leads to disconnected treatment approaches and missed opportunities for holistic patient care.

[0013] Patient data management presents another crucial challenge in contemporary healthcare systems. The growing importance of genetic information in personalized medicine has created new complexities in data handling and interpretation. Healthcare providers struggle with efficiently formatting and sharing patient data across various stakeholders, including insurance companies, government authorities, and NGOs. Furthermore, maintaining comprehensive health trends for individual patients over time remains problematic, limiting the healthcare system's ability to provide truly personalized and preventive care.

[0014] Access to healthcare resources represents a significant challenge in current medical practice. Healthcare providers and patients alike face difficulties in accessing accurate, up- to-date information about available healthcare facilities, specialist expertise, and treatment costs. The lack of consolidated information about expert specialists and their success rates, combined with limited access to details about treatment costs, financing options, and government healthcare schemes, often results in suboptimal healthcare decisions. Additionally, information about emergency medical services and insurance options with their claim settlement ratios is frequently fragmented or incomplete, creating barriers to efficient healthcare delivery.

[0015] The diverse requirements of medical specialties present unique challenges in diagnostic device development. Current diagnostic tools often fail to address the specific needs of different medical specialties, such as ophthalmologists requiring digital spectacles or ENT specialists needing integrated otoscopes. This one-size-fits-all approach to medical devices limits the effectiveness of specialty care and creates inefficiencies in clinical practice.

[0016] Current healthcare systems also show significant limitations in providing comprehensive patient guidance. There is a notable gap in systems capable of delivering personalized recommendations for diet based on factors such as age, sex, and BMI, or suggesting appropriate exercises based on specific health conditions and occupational requirements. Furthermore, the ability to recommend preventive health check-ups based on multiple factors including age, sex, lifestyle, work profile, and family history remains limited, hampering preventive healthcare efforts.

[0017] Clinical workflow presents another area of significant challenge in current medical practice. Healthcare providers face considerable difficulties in conducting and documenting systematic physical examinations while maintaining engagement with patients. The correlation of multiple symptoms and examination findings often relies heavily on the practitioner's memory and experience, lacking systematic digital support. Real-time documentation during patient examination remains cumbersome, and the generation of standardized medical reports and referral notes often creates additional administrative burden.

[0018] Emergency healthcare support systems currently face substantial limitations. The ability to provide quick access to emergency care information and coordinate emergency referrals efficiently remains challenging. Healthcare providers often struggle to access critical patient information quickly during emergencies, potentially impacting the quality and speed of emergency care delivery. These limitations can have serious consequences in time-critical situations where immediate access to accurate patient information and rapid coordination of care are essential.

[0019] Technology integration in healthcare presents ongoing challenges that affect both providers and patients. The current healthcare ecosystem struggles with effectively integrating various medical devices, including basic tools like BP machines and ECG devices, as well as more advanced technologies like digital diagnostic tools and wearable devices. The connection and coordination with robotic devices for surgical procedures remain complex and often unreliable. Additionally, real-time data sharing between devices and systems faces significant technical and practical barriers, limiting the potential for comprehensive, integrated healthcare delivery.

[0020] These challenges collectively highlight the need for innovative solutions that can address the multifaceted requirements of modern healthcare delivery. The integration of advanced technology with medical practice must consider not only the technical aspects of diagnosis and treatment but also the practical needs of healthcare providers and the diverserequirements of patient care. Future solutions must bridge these gaps while maintaining usability, reliability, and effectiveness in clinical settings.Current Technological Gaps:

[0021] Several significant technological gaps exist in current diagnostic solutions. The integration of physical examination and digital analysis remains a particular challenge, as existing solutions struggle to effectively combine traditional examination techniques with modem digital capabilities. The lack of real-time guidance during physical examinations and diagnostic procedures represents another crucial gap, limiting the effectiveness of current diagnostic tools.

[0022] Multi-modal data integration presents ongoing challenges, as existing systems often fail to effectively combine information from different diagnostic modalities, including physical examination findings, imaging results, and patient history. The absence of adaptive learning capabilities in current diagnostic tools limits their ability to improve based on clinical experiences and outcomes. Furthermore, most existing solutions focus on specific aspects of diagnosis rather than providing comprehensive support throughout the diagnostic process.Future Directions:

[0023] The future of medical diagnostics requires comprehensive solutions that can address current limitations while leveraging emerging technologies. These solutions must integrate multiple diagnostic modalities into a single, user-friendly platform while providing realtime guidance and feedback during examinations. The ability to learn and adapt from clinical experiences will be crucial, as will the capacity to support both general practitioners and specialists effectively.

[0024] The development of connected healthcare solutions has enabled improvements in information sharing between providers, access to specialist expertise, patient monitoring, and healthcare delivery efficiency. However, significant opportunities remain for innovation in these areas, particularly in developing more integrated and intelligent diagnostic platforms.

[0025] The present invention addresses these challenges and opportunities by providing a novel diagnostic device that combines physical examination capabilities with advanced digital analysis, real-time guidance, and comprehensive support for healthcare practitioners. This integrated approach represents a significant step forward in medical diagnostics, offering potential benefits for healthcare providers, patients, and the broader healthcare system. The device's ability to provide real-time feedback, integrate multiple diagnostic modalities, and support clinical decision-making addresses many of the currentlimitations in medical diagnostics while paving the way for more efficient and accurate diagnostic processes.SUMMARY

[0026] This summary is provided to introduce concepts related to a diagnostic device and a method for guiding an operator in performing diagnostic procedures on a patient's body. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.

[0027] One embodiment of the present subject matter relates to a diagnostic device and method for guiding an operator in performing diagnostic procedures on a patient's body. The diagnostic device may comprise a main body with a top surface and a bottom surface, wherein the bottom surface is adapted to be placed in contact with one or more body parts of the patient; one or more user output modules positioned on the top surface for providing real-time guidance to the operator; a set of sensors comprising peripheral sensors and diagnostic sensors arranged on the bottom surface; a memory; and a processor configured to execute instructions stored in the memory. The processor may be configured to receive and process preliminary inputs about the patient through a preliminary data acquisition module; determine a target body region and a starting point for diagnosis based on preliminary inputs, one or more inputs received from the operator, or real-time feedback received from the patient during diagnostics; perform one or more scans of the target body region to determine its boundaries or specific organs within the target body region; process data from the one or more scans through a data acquisition module; and generate real-time instructions based on the processed data to guide the operator in maneuvering the device for diagnostic purposes and generate suggested diagnosis, investigations, or treatment.

[0028] One potential benefit of the diagnostic device and method is that it may provide real-time guidance to the operator, enabling them to efficiently and accurately perform diagnostic procedures on the patient's body. The use of AI / ML algorithms and advanced sensors may enhance the device's ability to detect and visualize abnormalities, potentially leading to improved patient outcomes. The modular design of the device may allow for customization based on specific diagnostic needs, while the ability to connect to other medical devices and EHR systems may facilitate seamless integration into existing healthcare workflows.BRIEF DESCRIPTION OF DRAWINGS

[0029] The detailed description is described with reference to the accompanying Figures. The same numbers are used throughout the drawings to refer like features and components.

[0030] Figure 1 illustrates a network implementation of a diagnostic device for guiding an operator in performing diagnostic procedures on a patient's body, in accordance with an embodiment of the present disclosure;

[0031] Figure 2a illustrates a pictorial diagram of the diagnostic device, in accordance with an embodiment of the present disclosure;

[0032] Figure 2b illustrates a pictorial diagram of the diagnostic device, in accordance with an embodiment of the present disclosure;

[0033] Figure 3 illustrates a block diagram of the diagnostic device, in accordance with an embodiment of the present disclosure;

[0034] Figure 4 illustrates a flowchart of the method for guiding an operator in performing diagnostic procedures on a patient's body, in accordance with an embodiment of the present disclosure;

[0035] Figure 5 illustrates the connectivity between the diagnostic device and various external resources, in accordance with an embodiment of the present disclosure;

[0036] Figure 6 illustrates the device-initiated guidance process wherein the diagnostic device automatically guides the operator, in accordance with an embodiment of the present disclosure; and

[0037] Figure 7 illustrates the doctor-initiated diagnostic process using a nine-region abdominal grid system, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0038] Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

[0039] Figure 1 illustrates a network implementation of a diagnostic device for guiding an operator in performing diagnostic procedures on a patient's body, in accordance with an embodiment of the present disclosure.

[0040] Figure 2a and 2b illustrates a pictorial diagram of the diagnostic device, in accordance with an embodiment of the present disclosure;

[0041] Referring to figure 1, 2a and 2b, a network implementation 100 of a diagnostic device 10 for guiding an operator in performing diagnostic procedures on a patient's body is illustrated, in accordance with an embodiment of the present subject matter. It must be noted that the device (10) can be of any shape and size depending on the application and use. The diagnostic device 10 comprises a main body (12) having a top surface (13) and a bottom surface (14), wherein the bottom surface (14) is adapted to be placed in contact with one or more body parts of the patient. A set of peripheral sensors (16) is arranged on the bottom surface (14) for initial detection of abnormalities, while one or more diagnostic sensors (18) are positioned at the bottom surface (14) for detailed examination and recording of relevant information. The diagnostic device 10 further includes one or more user output modules (22) positioned on the top surface (13) of the main body (12), configured to generate real-time audio and visual instructions to guide the operator in maneuvering the device over specific body parts. A memory (306) and a processor (302) are also provided, with the processor (302) configured to fetch and execute computer- readable instructions stored in the memory (306) for performing diagnostic procedures on one or more organs from a set of organs.

[0042] The diagnostic device 10 includes a preliminary data acquisition module (307) for receiving a set of preliminary inputs comprising patient's medical history and symptoms. An artificial intelligence / machine learning (AI / ML) engine (310) processes these preliminary inputs to determine a target body region and a starting point to initiate the diagnosis process. The device performs a first scan of the target body region using the peripheral sensors (16) to determine the boundaries of the target body region, followed by a second scan using both peripheral sensors (16) and diagnostic sensors (18) for detailed examination of specific organs within the determined boundaries.

[0043] In one embodiment, the diagnostic device 10 may be connected to user devices 104 or applications residing on the user devices 104 through a network 106. The network 106 may be a cellular communication network, such as the Internet, used by user devices 104, which may include mobile phones, tablets, or virtual devices. The user devices 104 may be any electronic device, communication device, image capturing device, machine, software, automated computer program, robot, or a combination thereof.

[0044] The user devices 104 may support communication over various types of networks, such as Wide Area Networks (WAN), the Internet, telephone networks (e.g., analog, digital, POTS, PSTN, ISDN, xDSL), mobile telephone networks (e.g., CDMA, GSM,NDAC, TDMA, E-TDMA, NAMPS, WCDMA, CDMA-2000, UMTS, 3G, 4G), radio networks, television networks, cable networks, optical networks (e.g., PON), satellite networks (e.g., VSAT), packet-switched networks, circuit-switched networks, public networks, private networks, and / or other wired or wireless communications networks configured to carry data. Networks 106 may support wireless local area network (WLAN) and / or wireless metropolitan area network (WMAN) data communications functionality in accordance with Institute of Electrical and Electronics Engineers (IEEE) standards, protocols, and variants such as IEEE 802.11 ("WiFi"), IEEE 802.16 ("WiMAX"), IEEE 802.20x ("Mobile-Fi"), and others.

[0045] The diagnostic device 10 incorporates a connection module (312) that enables integration with other medical devices and electronic health record (EHR) systems. This connection allows for retrieval of additional patient data and transmission of diagnostic information for further analysis or record-keeping. The device also includes a built-in camera (28) for capturing visual images during examinations and monitoring operator movements to provide feedback on correct device positioning.

[0046] Through the network implementation 100, the diagnostic device 10 can access various resources including central data repositories, personal storage devices, other medical devices, mobile devices, robotic devices for surgical use, search engines, clinical guidelines, and mapping services. This connectivity enables comprehensive diagnostic capabilities while maintaining data security and patient privacy in accordance with healthcare standards and regulations.

[0047] Referring to figure 3, an architecture diagram of the diagnostic device 10 is illustrated, in accordance with an embodiment of the present subject matter. The diagnostic device 10 comprises at least one processor (302), an I / O interface (304), and a memory (306). The memory (306) stores programmed instructions corresponding to multiple modules including a preliminary data acquisition module (307), a data acquisition module (308), an artificial intelligence / machine learning (AI / ML) engine (310), a connection module (312), and other modules (314).

[0048] The processor (302) serves as the central processing unit of the diagnostic device 10, orchestrating all computational tasks and data processing operations. It is configured to fetch and execute computer-readable instructions stored in the memory (306), enabling the coordinated operation of various modules and components within the device. The processor (302) manages real-time processing of sensor data, execution of AI / ML algorithms, generation of user instructions, and coordination of communication with external devices and systems.

[0049] The I / O interface (304) facilitates bidirectional communication between the diagnostic device 10 and external entities, including user devices, medical systems, and networks. This interface manages both input operations, such as receiving data from sensors and user inputs, and output operations, such as transmitting diagnostic results and connecting with external medical devices. The I / O interface (304) supports various communication protocols and standards, enabling seamless integration with existing healthcare infrastructure.

[0050] The memory (306) serves as the primary storage component of the diagnostic device 10, incorporating both volatile and non-volatile memory elements. The volatile memory components, including static random-access memory (SRAM) and dynamic random-access memory (DRAM), provide high-speed temporary storage for active processing tasks. The non-volatile memory elements, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and memory cards, ensure permanent storage of system software, reference databases, and patient records.

[0051] The preliminary data acquisition module (307) represents a crucial component responsible for collecting and processing initial patient information. This module handles the acquisition of various types of preliminary data, including patient demographics, medical history, current symptoms, and basic physiological parameters. It implements intelligent data collection protocols that guide operators through systematic information gathering, ensuring comprehensive and accurate initial assessment. The module incorporates validation algorithms to verify data completeness and accuracy before proceeding with diagnostic procedures.

[0052] Working in conjunction with the preliminary data acquisition module (307), the data acquisition module (308) manages the collection and processing of real-time sensor data during diagnostic procedures. This module interfaces directly with the peripheral sensors (16) and diagnostic sensors (18), coordinating the simultaneous acquisition of multiple data streams. The data acquisition module (308) implements sophisticated signal processing algorithms to ensure high-quality data collection, even in challenging examination conditions. It handles various types of sensor inputs, including audio signals from microphones, vibration data from sensors, images from the camera (28), and detailed scans from diagnostic sensors (18).

[0053] The artificial intelligence / machine learning (AI / ML) engine (310) represents the core intelligence of the diagnostic device 10. This sophisticated module employs advanced algorithms, including deep learning models such as convolutional neural networks (CNNs)and recurrent neural networks (RNNs), to analyze collected data and generate diagnostic insights. The AI / ML engine (310) processes inputs from multiple sources, including preliminary patient data, real-time sensor readings, and patient feedback, to provide comprehensive diagnostic support. It continuously learns and adapts from accumulated diagnostic experiences, improving its accuracy and effectiveness over time.

[0054] The connection module (312) enables seamless integration of the diagnostic device 10 with external medical systems and devices. This module implements various communication protocols and standards, allowing the device to interface with electronic health record (EHR) systems, other diagnostic equipment, and healthcare networks. The connection module (312) manages secure data transmission, ensuring the confidentiality and integrity of sensitive medical information while enabling efficient information exchange with authorized systems and practitioners.

[0055] The other modules (314) complement the core functionality of the diagnostic device 10 by providing essential supporting features and capabilities. These modules include components for user interface management, data storage and retrieval, device configuration, and system maintenance. The user interface management component controls the interaction between the operator and the device through the user output modules (22), ensuring intuitive and responsive operation. The data storage and retrieval component manages the organization and access of diagnostic data, patient records, and system information within the device's memory (306).

[0056] The memory (306) maintains a structured data repository (318) that stores various types of information essential for the device's operation. This includes reference databases of normal and abnormal physiological patterns, machine learning models, patient records, and diagnostic protocols. The data repository (318) is organized to enable efficient storage and retrieval of information while maintaining data integrity and security. Additional data (320) stored in the memory serves as a dynamic repository for processed data, intermediate results, and temporary information generated during diagnostic procedures.

[0057] The diagnostic device 10 implements a sophisticated workflow for processing and analyzing diagnostic data. When the peripheral sensors (16) detect initial signals, these inputs are first processed by the data acquisition module (308) to ensure signal quality and proper formatting. The processed signals are then analyzed by the AI / ML engine (310) using advanced machine learning algorithms. This analysis includes pattern recognition, anomaly detection, and correlation with reference data stored in the data repository (318). Based on this analysis, the AI / ML engine (310) generates appropriate guidance instructions for the operator.

[0058] The AI / ML engine (310) employs a multi-layered approach to data analysis and decision-making. The first layer involves real-time processing of sensor data to detect basic patterns and anomalies. This is followed by more sophisticated analysis using deep learning models that can identify complex patterns and correlations within the data. The engine also incorporates contextual information, including patient history and preliminary inputs, to provide more accurate and relevant diagnostic insights.

[0059] The signal processing capabilities of the diagnostic device 10 are particularly sophisticated when handling data from the peripheral sensors (16) and diagnostic sensors (18). For audio signals captured by the microphone array, the device employs advanced noise cancellation algorithms to isolate relevant physiological sounds from background noise. Vibration data undergoes specialized filtering and analysis to identify characteristic patterns associated with various medical conditions. Images and scans from the diagnostic sensors (18) are processed using advanced image enhancement techniques to improve clarity and detail.

[0060] To ensure accurate alignment and positioning during diagnostic procedures, the device implements a precise guidance system through the AI / ML engine (310). This system processes real-time data from the peripheral sensors (16) to determine the device's current position relative to anatomical landmarks. The engine then generates specific instructions, delivered through the user output modules (22), to guide the operator in achieving optimal device positioning for accurate diagnosis.

[0061] The connection module (312) plays a crucial role in enabling the device's integration with broader healthcare systems. It implements secure communication protocols for exchanging data with electronic health record systems, allowing for seamless integration of diagnostic results into patient records. The module also enables real-time consultation with remote medical experts by facilitating the transmission of live diagnostic data and images. This connectivity feature enhances the device's utility in telemedicine applications and remote healthcare settings.

[0062] The memory management system within the device ensures efficient handling of both temporary and permanent data storage requirements. Temporary data, such as realtime sensor readings and intermediate processing results, is managed using the volatile memory components for quick access and processing. Permanent data, including patient records, diagnostic results, and system configurations, is stored in non-volatile memory with appropriate backup and security measures.

[0063] The AI / ML engine (310) implements a sophisticated diagnostic pipeline that processes data through multiple stages of analysis. During the initial examination phase,the engine processes signals from the peripheral sensors (16) to establish baseline readings and detect any immediate anomalies. This preliminary analysis helps determine the optimal examination path and identifies areas requiring more detailed investigation. The engine employs specialized algorithms for different types of examinations, adapting its analysis based on the specific body part being examined and the type of diagnostic procedure being performed.

[0064] The processing of diagnostic data involves complex integration of multiple data streams. When the diagnostic sensors (18) are activated, the AI / ML engine (310) correlates their high-resolution data with the initial findings from the peripheral sensors (16). This multi-modal analysis enables more accurate detection and characterization of abnormalities. The engine uses advanced pattern recognition algorithms to identify subtle variations that might indicate underlying medical conditions, while also considering contextual information such as patient history and reported symptoms.

[0065] Real-time feedback processing represents another crucial capability of the AI / ML engine (310). During examination procedures, the engine continuously monitors patient feedback regarding pain or discomfort, integrating this information with sensor data to adjust the examination protocol dynamically. This adaptive approach ensures patient comfort while maintaining diagnostic accuracy. The engine can modify its guidance instructions based on patient feedback, suggesting alternative examination approaches when necessary.

[0066] The device's machine learning capabilities extend beyond basic pattern recognition. The AI / ML engine (310) employs sophisticated deep learning models, including specialized convolutional neural networks (CNNs) designed for medical image analysis. These networks are trained on extensive datasets of medical images and diagnostic data, enabling them to recognize subtle patterns and anomalies that might be difficult for human operators to detect. The engine continuously updates its models based on new data and validated diagnoses, improving its accuracy over time.

[0067] Data security and privacy features are integral to the device's architecture. The memory (306) implements robust encryption mechanisms to protect sensitive patient data both during storage and transmission. Access to different types of data is controlled through a sophisticated permissions system, ensuring that sensitive information is only accessible to authorized users. The connection module (312) implements secure communication protocols when exchanging data with external systems, maintaining compliance with healthcare data protection regulations.

[0068] The device incorporates advanced calibration and quality control mechanisms. Before each diagnostic session, the system performs automatic calibration of both peripheral sensors (16) and diagnostic sensors (18) to ensure optimal performance. This includes adjustment of sensor sensitivity, verification of signal quality, and validation of system parameters. The AI / ML engine (310) monitors sensor performance during examinations, flagging any anomalies that might affect diagnostic accuracy.

[0069] Resource management within the device is handled through sophisticated scheduling and prioritization algorithms. The processor (302) implements dynamic resource allocation, ensuring that critical diagnostic functions receive priority processing while maintaining responsive system operation. The memory management system optimizes data storage and retrieval operations, implementing efficient caching mechanisms for frequently accessed data while ensuring proper storage of long-term records.

[0070] The integration capabilities of the device extend beyond basic data exchange. The connection module (312) implements advanced protocols for real-time collaboration with other medical devices and systems. This includes synchronization with imaging systems, integration with patient monitoring devices, and coordination with therapeutic equipment. The module supports various healthcare interoperability standards, enabling seamless integration with existing medical infrastructure.

[0071] The system architecture of the diagnostic device 10 includes sophisticated error handling and reliability features. The processor (302) implements comprehensive error detection and recovery mechanisms to ensure reliable operation during diagnostic procedures. This includes monitoring of sensor performance, validation of data integrity, and automatic recovery from various types of system errors. The device maintains detailed system logs through the memory (306), recording operational parameters, error conditions, and system events for troubleshooting and quality assurance purposes.

[0072] The AI / ML engine (310) incorporates advanced learning capabilities that enable it to improve its diagnostic accuracy over time. The engine maintains performance metrics for different types of diagnoses, tracking the accuracy of its predictions and recommendations. When diagnostic outcomes are confirmed through other medical procedures or specialist consultations, this feedback is used to refine the engine's machine learning models. This continuous learning process helps optimize the device's diagnostic capabilities and reduces the likelihood of false positives or missed diagnoses.

[0073] The connection module (312) supports sophisticated telemedicine capabilities, enabling remote diagnostic sessions and expert consultations. During remote sessions, the module manages real-time streaming of diagnostic data, including sensor readings, images, and analysis results. The system supports bidirectional communication, allowing remote specialists to provide guidance and feedback during examinations. This capability is particularly valuable in settings where specialist expertise may not be locally available.

[0074] The device implements advanced user interface adaptation through the other modules (314). The system can adjust its interface based on operator expertise levels, environmental conditions, and specific examination requirements. This includes modification of display layouts, adjustment of audio guidance volume and frequency, and customization of instruction detail levels. The interface adaptation ensures optimal usability across different operating conditions and user skill levels.

[0075] Memory management in the device incorporates sophisticated data lifecycle management features. The memory (306) implements automated archiving and cleanup procedures to maintain optimal system performance while preserving essential diagnostic data. The system uses intelligent data compression algorithms to minimize storage requirements while maintaining quick access to frequently used information. Regular integrity checks ensure the reliability of stored data and prompt appropriate maintenance actions when necessary.

[0076] The device architecture supports modular expansion through carefully designed hardware and software interfaces. The processor (302) and memory (306) are dimensioned to accommodate future additions of new sensor types, analytical capabilities, and diagnostic functions. The system software is structured to allow seamless integration of new modules and updates to existing functionality without disrupting core operations.

[0077] Performance optimization is achieved through sophisticated scheduling algorithms implemented by the processor (302). The system dynamically balances processing loads across different tasks, ensuring responsive operation during diagnostic procedures while maintaining background processing of analytical tasks. The memory management system implements intelligent caching strategies to minimize data access latency and optimize system responsiveness.

[0078] The diagnostic device 10 includes comprehensive reporting capabilities managed through the other modules (314). The system can generate detailed diagnostic reports incorporating various types of data, including sensor readings, analysis results, and examination findings. Reports can be customized based on recipient requirements, whether for patient records, specialist referrals, or research purposes. The connection module (312)manages secure distribution of these reports to appropriate healthcare systems and providers.

[0079] System maintenance and updates are handled through sophisticated management features. The device supports remote monitoring of system health, allowing preventive maintenance and early detection of potential issues. Software updates can be deployed securely through the connection module (312), enabling enhancement of system capabilities and implementation of new diagnostic protocols without requiring physical device modifications.

[0080] The architecture supports extensive data analytics capabilities through the AVME engine (310). The system can analyze patterns across multiple diagnostic sessions, identifying trends and correlations that might be relevant for population health management or research purposes. These analytics capabilities are implemented with strict privacy controls, ensuring that any data used for analysis is appropriately anonymized and protected.

[0081] The device implements comprehensive audit trails for all system operations. Every diagnostic procedure, data access, and system modification is logged with appropriate timestamps and user identification. These audit trails support quality assurance processes, help maintain regulatory compliance, and provide valuable information for system optimization and troubleshooting.

[0082] Finally, the system architecture includes provisions for integration with future healthcare technologies. The processor (302) and memory (306) architectures are designed to accommodate emerging diagnostic techniques and analytical methods. The connection module (312) supports evolving healthcare interoperability standards, ensuring the device can remain relevant and effective as healthcare technology continues to advance.

[0083] This comprehensive architectural design enables the diagnostic device 10 to provide sophisticated diagnostic capabilities while maintaining reliability, security, and adaptability to evolving healthcare needs.

[0084] Now referring to figure 4 illustrates a flowchart of the method for guiding an operator in performing diagnostic procedures on a patient's body, in accordance with an embodiment of the present disclosure.

[0085] The present disclosure relates to a method for guiding a doctor in performing diagnostic procedures on a patient's body using a diagnostic device (10). Referring to Figure 1, the diagnostic device (10) comprises a main body (12) having a top surface (13) and a bottom surface (14), one or more user output modules (22) positioned on the topsurface (13), a set of peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14), a memory (306), and a processor (302).

[0086] In step 402, the method involves placing the bottom surface (14) of the diagnostic device (10) in contact with the patient's body. This step ensures proper positioning of the diagnostic device (10) for collecting diagnostic data. The main body (12) is designed to be modular, with interchangeable sensor modules that can be attached based on specific requirements of the operator or type of diagnostic procedure being performed. The bottom surface (14) is adapted to conform to the contours of the patient's body, providing a stable and comfortable interface for diagnostic procedures.

[0087] In step 404, the method involves receiving and processing preliminary inputs about the patient by a preliminary data acquisition module (307). These inputs include patient's medical history, symptoms, age, sex, weight, occupation, and other relevant information. The data acquisition module (308) also receives a first set of signals from the set of peripheral sensors (16) and diagnostic sensors (18). The peripheral sensors (16) comprise an array of microphones or vibration sensors for detecting changes in sound or vibration patterns, while the diagnostic sensors (18) include high-frequency ultrasound transducers or miniaturized X-ray detectors for detailed visualization.

[0088] In step 406, the method involves determining a target body region and a starting point for diagnosis based on the preliminary inputs, using an artificial intelligence / machine learning (AI / ML) engine (310). The AI / ML engine (310) employs deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze these inputs and determine the optimal starting point. The engine adapts its algorithms to account for variations in patient physiology, environmental conditions, and operator skill levels.

[0089] In step 408, the method involves generating, by the AI / ML engine (310), a first set of audio, visual, and haptic instructions for guiding the operator to align the device (10) over the target body region. The user output modules (22), comprising a high-resolution touchscreen display and spatial audio speakers, present these instructions. The spatial audio speakers provide directional audio cues to guide the operator's hand movements during the scanning process, while a haptic feedback system integrated into the main body (12) provides tactile guidance.

[0090] In step 410, the method involves performing a first scan of the target body region by capturing real-time data through the peripheral sensors (16). During this scan, the device maintains a database of normal and abnormal sound or vibration patterns for different organs and body regions to enhance the accuracy of abnormality detection. Theprocessor incorporates advanced signal processing techniques, including adaptive noise cancellation to reduce environmental interference and improve data quality.

[0091] During the first scan, the device (10) actively compares detected patterns against its comprehensive database of normal and abnormal sound / vibration signatures for different organs and body regions. This database, continuously updated through machine learning, serves as a critical reference point for real-time abnormality detection and pattern recognition.

[0092] The processor (302) implements sophisticated feature extraction methods to identify relevant patterns in the collected data streams. These methods include wavelet analysis for time-frequency decomposition, statistical feature extraction for pattern recognition, and morphological analysis for structural feature identification. The system's data fusion techniques combine information from multiple sensors using both probabilistic and deep learning approaches, enabling a more nuanced and comprehensive diagnostic analysis.

[0093] In step 412, the method involves processing, by the AI / ML engine (310), the collected data from the first scan to generate a second set of real-time audio and visual instructions, if necessary, for conducting detailed diagnostics. The AI / ML engine (310) utilizes image enhancement algorithms to improve the clarity and detail of visualizations generated from the diagnostic sensors' (18) data. The touchscreen display shows real-time visualizations of the scanned areas, highlighting potential abnormalities and providing intuitive controls for the operator.

[0094] In step 414, the method involves performing a second scan comprising iterative examination of each organ in the subset of organs within the target body region. During this step, the diagnostic sensors (18) are selectively activated based on the results of the first scan. The device (10) connects via a connection module (312) to other medical devices or electronic health record (EHR) systems to retrieve additional patient data and enhance the accuracy of the AI / ML engine's (310) analysis.

[0095] In step 416, the method involves receiving feedback from the patient regarding pain or discomfort through the data acquisition module (308). The built-in camera (28) captures visual images of the patient's body during both the first and second scans, and the AI / ML engine (310) processes these visual images in conjunction with other data streams to improve the accuracy of organ boundary detection and abnormality identification. The camera (28) also monitors the operator's movements to provide real-time feedback on correct positioning. In an embodiment, the operator may be instructed to conduct a test on the patient and the camera (28) may be used for capturing visuals of the patient whileconducting the tests on the patient. These tests may be for example Romberg’s test, straight leg raise (SLR) test, any reflexes and the like. Optionally, the operator may receive realtime instructions to appropriately perform the test. The test results are captured in real-time by the camera (28) and or sensors (18) and processed by the AI / ML engine (310).

[0096] In step 418, the method involves processing, by the AI / ML engine (310), the second set of signals, along with the patient feedback, to identify abnormalities and determine a successive organ to be examined. The engine generates personalized diagnostic protocols based on patient-specific factors identified in the preliminary inputs and during the diagnostic process. The AI / ML engine (310) provides preliminary diagnoses with confidence levels and supporting evidence, suggesting additional tests if the diagnosis is not conclusive.

[0097] In step 420, the method involves outputting instructions for navigating to the successive organ. The haptic feedback system works in conjunction with the audio and visual instructions to enhance the operator's ability to accurately position and move the device (10). The AI / ML engine (310) continuously learns and improves its performance based on accumulated data from multiple diagnostic sessions, providing explanations for its decisions and recommendations to enhance transparency and assist in operator training.

[0098] In step 422, the method involves processing, by the AI / ML engine (310), all collected data streams and the patient feedback to generate comprehensive diagnostic insights. The processor (302) employs data fusion techniques to integrate information from multiple sensors and data streams for accurate diagnostic analysis. The device (10) transmits the collected diagnostic data to connected systems for further analysis or recordkeeping and receives real-time updates or adjustments to the diagnostic process based on input from connected systems or remote medical professionals.

[0099] In step 424, the method involves processing, by the AI / ML engine (310), the combined data streams from multiple sources along with patient feedback. This comprehensive analysis serves two critical purposes: identifying specific abnormalities within the examined organ and determining the logical progression to successive organs for examination. The AI / ML engine (310) employs advanced pattern recognition algorithms to correlate symptoms, sensor data, and anatomical relationships to create a comprehensive diagnostic pathway. For example, when examining abdominal organs, the detection of tenderness and inflammation in the right lower quadrant might prompt the system to prioritize examination of adjacent organs to rule out spreading infection or related pathologies.

[0100] Step 426 builds upon this analysis by generating a fourth set of real-time instructions for navigating the device (10) to the successive organ. These instructions are dynamically generated based on the current findings and anatomical relationships. The system creates a precise navigation pathway that considers patient comfort, optimal approach angles, and the most efficient examination sequence.

[0101] In step 428, the AVME engine (310) performs a comprehensive analysis of all collected data streams and patient feedback to generate preliminary diagnoses. Each diagnosis is assigned a confidence level based on the correlation strength between observed symptoms, collected data, and known disease patterns. When confidence levels fall below predetermined thresholds, the system automatically generates recommendations for additional diagnostic tests, such as laboratory studies or imaging procedures. The engine also assesses the urgency of findings to provide appropriate treatment recommendations and identify cases requiring emergency intervention. For example, in a patient with acute abdominal pain, the system might recognize patterns suggestive of testicular torsion and flag it as requiring urgent surgical evaluation. In an embodiment, this data may be sent to a specialist in that domain for seeking his expert opinion. For example, sending X-Ray images to Radiologist in real-time for his expert opinion.

[0102] Step 430 involves the systematic transmission of collected diagnostic data through the connection module (312) to various healthcare information systems. This data transmission follows secure healthcare protocols and ensures that critical information is simultaneously updated across multiple platforms. The system transmits raw sensor data, processed results, and diagnostic conclusions to connected medical devices for immediate use in treatment planning. Electronic health record systems receive structured diagnostic reports and relevant imaging data. The operator's personal storage device maintains a local copy of the examination data for immediate access, while the central data repository archives the complete diagnostic session for future reference and analysis.

[0103] Finally, in step 432, the user output modules (22) display a comprehensive summary of the diagnostic process. This display includes dynamic visualizations of scanned areas with abnormalities clearly highlighted and annotated. The system generates detailed maps of examined regions with color-coded overlays indicating areas of concern. Treatment options are presented in a prioritized list based on diagnosis confidence levels and urgency. The system also provides geographically relevant information about nearby medical facilities capable of providing specialized care for the identified conditions. Additionally, the display includes information about available healthcare resources, such asspecialist consultations, rehabilitation services, or home care options, tailored to the specific diagnosis and patient needs.

[0104] These final steps in the diagnostic process ensure that the device (10) not only collects and analyzes diagnostic data but also facilitates immediate action through appropriate healthcare channels while providing comprehensive, accessible information to both healthcare providers and patients. The integration of data analysis, healthcare system connectivity, and intuitive information display creates a seamless pathway from diagnosis to treatment initiation.

[0105] The diagnostic device (10) and its method of operation offer several innovative features that enhance the diagnostic process. The modular design allows for future upgrades or additions of new sensor technologies without requiring replacement of the entire device. The self-calibration feature ensures optimal performance of the attached sensor modules before each diagnostic procedure, while the processor automatically detects the type of sensor modules attached and adjusts its processing algorithms accordingly.

[0106] Furthermore, the device provides comprehensive support for various medical contexts. It can guide patient counseling by providing information about surgeries, approximate costs, nearby hospitals, and expert doctors according to respective illnesses. The system also assists with insurance-related matters by formatting and providing necessary data to insurance companies and suggesting policies based on patient health conditions and claim settlement ratios.

[0107] The device's connectivity capabilities extend to various resources, including central data repositories, doctor's personal storage devices, other medical devices (such as BP machines, digital spectacles, ECG machines, and wrist belts), mobile devices, robotic devices for surgical use, search engines, clinical guidelines, and mapping services. This comprehensive integration ensures that the diagnostic process benefits from a wide range of information sources and tools.

[0108] An essential feature of the device is its ability to provide different levels of functionality based on the operator's expertise. For general practitioners, it helps in diagnosis, treatment planning, expedite the treatment in case of emergencies and specialist referrals, with hardware and software designed for their scope of work. For specialists, the device can be customized according to their specialty, such as providing digital spectacles for ophthalmologists or specialized scopes for ENT specialists.

[0109] The device also supports broader healthcare objectives by facilitating access to government schemes, NGO support, and insurance programs. It can gather and format datarequired for submissions to government authorities or NGOs, providing relevant contact information and documentation. This comprehensive approach ensures that patients receive not only accurate diagnostic services but also guidance on accessing various support systems available to them.

[0110] The method's success relies heavily on the sophisticated AI / ML engine (310), which continuously learns from each diagnostic session. The engine adapts its algorithms based on accumulated experience, leading to increasingly accurate and personalized diagnostic protocols. By providing clear explanations for its recommendations, the system maintains transparency while serving as an effective training tool for healthcare providers and can also be used for medical education. Also, the device (10) can be used for space expeditions, research or treating human settlements at extra-terrestrial locations.

[0111] The integration of multiple feedback mechanisms - audio, visual, and haptic - represents a significant advancement in operator guidance. The spatial audio system provides directional cues without requiring constant visual attention, while the haptic feedback system offers tactile guidance for precise device positioning. These features, combined with the high-resolution touchscreen display, create an intuitive and efficient interface for operators of varying expertise levels.

[0112] In conclusion, the method for guiding an operator in performing diagnostic procedures using the diagnostic device (10) represents a comprehensive approach to modern medical diagnostics. By combining advanced sensing technologies, artificial intelligence, and user-friendly interfaces with extensive connectivity and support features, the system not only improves diagnostic accuracy but also enhances the overall healthcare delivery process. The device's adaptability to different medical specialties and its ability to facilitate access to various healthcare resources make it a valuable tool in advancing patient care and medical practice.

[0113] Figure 5 illustrates the various resources and systems that the diagnostic device (10) can access and interact with through its connection module (312). This comprehensive connectivity infrastructure enables enhanced diagnostic capabilities and seamless integration with the healthcare ecosystem.

[0114] At the heart of the device's connectivity is its interface with the Central Data Repository, which serves as the cornerstone for data management and storage. This repository not only maintains patient medical records and diagnostic data but also houses the extensive database of normal and abnormal sound and vibration patterns that the AI / ML engine (310) relies upon. The system continuously updates this repository with newlearning data from diagnostic sessions, creating an ever-expanding knowledge base for improved diagnostic accuracy.

[0115] The device (10) maintains a crucial connection to the doctor's personal storage devices, including CPUs and laptops. This connection enables physicians to maintain their own diagnostic protocols, settings, and patient records while ensuring immediate access to critical data even in offline scenarios. The personal storage integration allows doctors to develop and refine their diagnostic approaches based on their specific clinical experiences and specialties.

[0116] A significant feature of the device's connectivity is its seamless integration with other medical devices. The connection module (312) facilitates real-time data exchange with various medical equipment, including digital blood pressure monitors, specialized digital spectacles, ECG machines, and wearable health monitors. This multi-device integration enables synchronized data acquisition and analysis, providing a comprehensive view of the patient's health status during diagnostic procedures.

[0117] Mobile device integration extends the device's functionality beyond the immediate clinical setting. Through secure connections to doctors' mobile devices, the system enables remote access to diagnostic data, real-time notifications, and secure communication channels between healthcare providers and patients. This mobile integration ensures that critical diagnostic information remains accessible whenever and wherever needed.

[0118] The device's interface with robotic systems represents a significant advancement in medical technology integration. By connecting with surgical robots and automated medical devices, the system supports precision guidance during procedures and enables real-time data exchange during surgical interventions. This robotic integration facilitates both minor procedures and more complex surgical operations, enhancing the accuracy and efficiency of medical interventions.

[0119] The connection module (312) provides secure access to internet-based resources, including medical knowledge bases and Al-powered search engines. This connectivity enables real-time access to the latest medical research, drug information, and treatment protocols, supporting evidence-based medical practice. The integration with services like ChatGPT enhances the system's decision support capabilities, providing additional context and insights for complex diagnostic scenarios.

[0120] Clinical guideline integration ensures that diagnostic and treatment decisions align with established medical standards. Through direct access to NHS guidelines and other standardized protocols, the device maintains current best practices across various medicalspecialties. This integration helps healthcare providers maintain compliance with standard operating procedures while delivering optimal patient care.

[0121] The device's integration with geolocation services adds a crucial logistical dimension to patient care. Through mapping service integration, the system can identify and provide navigation to the nearest specialized medical facilities, pharmacies, and emergency services. This capability proves particularly valuable in urgent care scenarios and when coordinating patient referrals to specialists.

[0122] The entire connectivity framework operates under robust security protocols, ensuring data privacy and regulatory compliance. The connection module (312) supports both wireless and wired connections with automatic failover capabilities, guaranteeing uninterrupted operation. The AI / ML engine (310) leverages this comprehensive connectivity to enhance its diagnostic capabilities, accessing real-time updates and distributed computing resources as needed.

[0123] This interconnected framework positions the device (10) as a central hub within the modem healthcare ecosystem. The system's modular architecture ensures future expandability, allowing for the integration of new connectivity options and resources as they emerge. This forward-looking design approach ensures the device remains technologically relevant while continuing to facilitate improved patient care through enhanced data access and seamless medical infrastructure integration.

[0124] Figure 6 illustrates the device's intelligent guidance system for automated diagnostic navigation, specifically demonstrating how the device (10) autonomously guides the operator to specific anatomical locations. This figure particularly emphasizes the process of locating cardiac examination points, using the apex beat location as a primary example.

[0125] The guidance process begins with the device's display screen providing clear visual instructions to the operator, directing them to place the device at a specific reference point - in this case, the middle of the sternum. This initial positioning is crucial as it establishes the primary anatomical landmark from which subsequent movements will be guided. The device utilizes its four corner-mounted peripheral sensors (16) to confirm proper placement at this reference point, employing sound wave analysis and pressure detection to verify the position relative to the sternum's bony landmarks.

[0126] Once the reference position is established, the AI / ML engine (310) initiates a systematic guidance protocol. The high-resolution touchscreen display shows a real-time anatomical overlay, while the spatial audio speakers provide directional cues guiding the operator "towards the left lower side below the nipple" - the typical location of the apexbeat. This guidance combines multiple sensory inputs: visual indicators on the screen show the intended movement path, haptic feedback through the device's integrated system provides tactile directionality, and spatial audio cues offer additional movement guidance. For instance, when guiding towards the apex beat, the system might provide a progressively intensifying haptic pulse as the device approaches the correct location. In another embodiment, instead of haptic pulse, the display of the device 10 may be used for guiding the operator to reach the target location.

[0127] The device employs its peripheral sensors (16) throughout this movement to constantly monitor position and orientation. These sensors detect subtle changes in tissue density, intercostal spaces, and underlying anatomical structures to confirm the device's current position relative to its target. The diagnostic sensors (18) simultaneously begin gathering preliminary data, which the AI / ML engine (310) processes to refine the positioning guidance. For example, when approaching the apex beat, the sensors can detect the strengthening of cardiac impulses, helping to precisely locate the point of maximal impulse.

[0128] To ensure accuracy, the device incorporates real-time feedback mechanisms. The built-in camera (28) tracks the device's movement relative to external anatomical landmarks, while the pressure sensors adjust sensitivity based on the applied force to maintain optimal contact. The system can detect if the operator deviates from the ideal path and immediately provides corrective guidance. For instance, if the operator moves too far laterally when seeking the apex beat, the device generates immediate feedback through all three modalities - visual alerts on the screen, directional audio cues, and corrective haptic pulses.

[0129] The guidance system demonstrates particular sophistication in adapting to individual patient variations. For example, in patients with different body habitus, the device adjusts its guidance parameters based on the initial measurements and continuous feedback. In a patient with a larger chest wall, the system might modify the expected distance from the sternal reference point to the apex beat, while in a patient with dextrocardia, the AI / ML engine (310) would recognize the reversed cardiac position and appropriately adjust its guidance to the right side of the chest.

[0130] Throughout this process, the device maintains constant communication with its connected systems through the connection module (312). Real-time data about the device's position and movement can be transmitted to connected displays or recording systems, allowing for documentation of the examination process. The system can also access storedanatomical data and previous examination records to refine its guidance based on patientspecific factors or previously successful examination patterns.

[0131] This intelligent navigation system exemplifies the device's capability to transform complex anatomical knowledge into practical, real-time guidance. Whether locating specific cardiac examination points, identifying optimal positions for auscultation, or guiding the examination of other anatomical structures, the system combines sophisticated sensor technology with intuitive user feedback to enhance the accuracy and efficiency of physical examination procedures.

[0132] Figure 7 illustrates the device's interactive guidance system for doctor-initiated examination, specifically demonstrating the systematic approach to abdominal examination using a nine -region grid system. This figure showcases how the device (10) responds to and enhances doctor-directed diagnostic procedures, with particular emphasis on the examination of Region 7 (right lower quadrant) as an exemplary case.

[0133] The examination process begins with the device displaying clear instructions to place it on the umbilicus, which serves as the central reference point (Region 5 in the nine- region grid). When positioned here, the device's four corner-mounted peripheral sensors (16) immediately begin mapping the abdominal dimensions. This mapping process utilizes a combination of ultrasound, pressure sensitivity, and spatial positioning data to create a precise topographical map of the patient's abdomen. For instance, in a patient presenting with suspected appendicitis, this initial mapping helps establish the relative positions of key anatomical landmarks and potential areas of interest.

[0134] The device's intelligent scanning system then guides the doctor through a systematic measurement of the abdomen in all directions. Using real-time audio-visual feedback, the system instructs upward movement to Region 2 (epigastric), downward to Region 8 (hypogastric), rightward to Region 4 (right lumbar), and leftward to Region 6 (left lumbar). During this calibration process, the AVML engine (310) processes data streams from both peripheral sensors (16) and diagnostic sensors (18) to create a comprehensive baseline assessment. For example, the system might detect and map variations in tissue density, fluid collections, or areas of increased temperature that could indicate underlying pathology.

[0135] Following this initial mapping, when the doctor moves the device to Region 7 (right lower quadrant), the system automatically recognizes the new position through its sophisticated spatial awareness capabilities. The device immediately initiates a regionspecific diagnostic protocol, adapting its sensor sensitivity and data collection parameters to the anatomical structures typically found in this area. For instance, in the examination ofRegion 7, the device might automatically adjust its ultrasound frequency to optimize visualization of the appendix, cecum, and terminal ileum.

[0136] The device demonstrates remarkable adaptability in its diagnostic approach based on the specific clinical scenario. For example, if examining a patient with suspected appendicitis, the AI / ML engine (310) will guide the doctor through specific examination points within Region 7, including McBurney's point. The system provides real-time feedback about tissue changes, temperature variations, and areas of increased tenderness, correlating these findings with the patient's reported symptoms and clinical history.

[0137] The integration of multiple data streams enables sophisticated diagnostic capabilities. While in Region 7, the device simultaneously processes:- Temperature gradients detected by thermal sensors- Tissue elasticity measurements from pressure sensors- Deep tissue imaging from the diagnostic ultrasound- Surface contour mapping from the peripheral sensors- Patient feedback about pain or discomfort

[0138] Visual data from the built-in camera (28) All these inputs are processed in realtime by the AI / ML engine (310) to generate preliminary diagnostic suggestions.

[0139] Based on the initial findings, the device can automatically suggest additional diagnostic modalities. For instance, if the preliminary examination of Region 7 suggests possible appendicitis, the device might automatically activate its high-resolution ultrasound mode, guiding the doctor to perform a detailed sonographic examination of the area. The system provides real-time guidance for optimal probe positioning and pressure application, ensuring high-quality diagnostic imaging.

[0140] The device's artificial intelligence capabilities enable it to correlate multiple findings and suggest appropriate next steps. For example, after detecting localized tenderness, increased temperature, and characteristic ultrasound findings in Region 7, the system might:- Generate a preliminary diagnosis with confidence levels- Suggest additional targeted examinations- Recommend specific laboratory tests- Propose appropriate imaging studies- Identify nearby surgical facilities if urgent intervention is needed

[0141] The system maintains its role as a diagnostic aid while preserving the doctor's medical authority. After presenting its analysis and recommendations, the device allows the doctor to refine or modify the diagnosis, adjust treatment recommendations, and customizethe patient care plan. For example, the doctor can edit suggested prescriptions, modify dosages, or add specific instructions based on their clinical judgment and patient- specific factors.

[0142] This doctor-directed diagnostic approach exemplifies the device's ability to enhance clinical examination while maintaining the crucial role of medical expertise and clinical judgment. The system serves as an intelligent assistant, providing data-driven insights and suggestions while allowing the healthcare provider to make final diagnostic and treatment decisions based on their comprehensive understanding of the patient's condition.

[0143] Although implementations for the present subject matter, have been described in language specific to structural features and methods, it must be understood that the claims are not limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of the present subject matter.

Claims

CLAIMS:

1. A device (10) for guiding a doctor for diagnosis, investigations, and treatments of a patient, the device (10) comprising: a main body (12) having a top surface (13) and a bottom surface (14), wherein the bottom surface (14) is adapted to be placed in contact with one or more body parts of the patient; one or more user output modules (22) positioned on the top surface (13) for providing real-time guidance to the doctor; a set of sensors comprising peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14); a memory (306); and a processor (302) configured to execute instructions stored in the memory (306) for: receiving and processing preliminary inputs about the patient by a preliminary data acquisition module (307); determining a target body region and a starting point for diagnosis based on preliminary inputs, one or more inputs received from doctor, or real-time feedback received from the patient during diagnostics; performing one or more scans of the target body region to determine its boundaries or specific organs within the target body region; processing data from the one or more scans by a data acquisition module (308); and generating real-time instructions based on the processed data to guide the operator in maneuvering the device (10) for diagnostic purposes and generate suggested diagnosis, investigations, or treatment.

2. The diagnostic device (10) of claim 1, wherein: the peripheral sensors (16) are configured for initial detection of abnormalities and comprise an array of microphones or vibration sensors for detecting changes in sound or vibration patterns associated with abnormalities; the diagnostic sensors (18) are configured for detailed examination and comprise high-frequency ultrasound transducers or miniaturized X-ray detectors for providing detailed visualization of abnormalities;the device (10) maintains a database of normal and abnormal sound or vibration patterns for different organs and body regions to enhance the accuracy of abnormality detection; and the processor (302) is configured to analyze these patterns in real-time to adjust the generated instructions and provide immediate feedback to the operator.

3. The diagnostic device (10) of claim 1, wherein the instructions executed by the processor (302) for performing the first scan comprise: generating, by an artificial intelligence / machine learning (AI / ML) engine (310), a first set of audio or visual instructions to guide the operator in maneuvering the device (10) over the target body region; capturing, by the peripheral sensors (16), a first data stream in real-time during the first scan based on the first set of instructions; processing, by the AI / ML engine (310), the first data stream in real-time to generate feedback for the operator to maneuver the device (10) and record the boundaries of the target body region; and wherein the AI / ML engine (310) employs deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to process the first data stream and generate the feedback.

4. The diagnostic device (10) of claim 1, wherein the instructions executed by the processor (302) for performing the second scan comprise iteratively examining each organ in a subset of organs by: generating, by the AI / ML engine (310), a second set of audio or visual instructions to guide the operator in maneuvering the device (10) over a specific organ, based on the determined boundaries of the target body region and the preliminary inputs; receiving a second data stream from the peripheral sensors (16) and a third data stream from the diagnostic sensors (18); processing, by the AI / ML engine (310), the second and third data streams to generate a third set of real-time audio and visual instructions for conducting detailed diagnostics of the organ; receiving feedback from the patient regarding pain or discomfort; processing, by the AI / ML engine (310), the second data stream, third data stream, and patient feedback to identify any abnormalities associated with the organ and to determine a successive organ to be examined; and generating a fourth set of real-time instructions for navigating the device (10) to the successive organ.

5. The diagnostic device (10) of claim 1, further comprising: a built-in camera (28) for capturing visual images of the patient's body during both the first and second scans; wherein the processor (302) is configured to process these visual images in conjunction with other data streams to improve the accuracy of organ boundary detection and abnormality identification; and wherein the camera (28) is also used to monitor the operator's movements and provide real-time feedback on the correct positioning and maneuvering of the device (10).

6. The diagnostic device (10) of claim 1, further comprising: a connection module (312) configured to connect to other medical devices or electronic health record (EHR) systems; wherein the processor (302) is further configured to: retrieve additional patient data from these connected systems to enhance the preliminary inputs and improve the accuracy of the AI / ML engine's (310) analysis; transmit the collected diagnostic data, including all data streams, to these connected systems for further analysis or record-keeping; and receive real-time updates or adjustments to the diagnostic process based on input from connected systems or remote medical professionals.

7. The diagnostic device (10) of claim 1, wherein: the main body (12) is modular, with interchangeable sensor modules that can be attached based on specific requirements of the operator or a type of diagnostic procedure being performed; the processor (302) is configured to automatically detect the type of sensor modules attached and adjust its processing algorithms and generated instructions accordingly; the device (10) includes a self-calibration feature that ensures optimal performance of the attached sensor modules before each diagnostic procedure; and the modular design allows for future upgrades or additions of new sensor technologies without requiring replacement of the entire device (10).

8. The diagnostic device (10) of claim 1, wherein the processor (302) is further configured to: provide preliminary diagnoses based on the analysis of all collected data streams and patient feedback;display these preliminary diagnoses on the user output modules (22) along with confidence levels and supporting evidence; suggest additional tests or examinations if the confidence level for a diagnosis falls below a predetermined threshold; and continuously update and refine these diagnoses throughout the diagnostic process as more data is collected and analyzed.

9. The diagnostic device (10) of claim 1, wherein the processor (302) incorporates advanced signal processing techniques, including: adaptive noise cancellation to reduce environmental interference and improve the quality of data collected by the sensors during both the first and second scans; image enhancement algorithms to improve the clarity and detail of visualizations generated from the diagnostic sensors' (18) data; feature extraction methods to identify relevant patterns or abnormalities in the collected data streams; and data fusion techniques to integrate information from multiple sensors and data streams for a more comprehensive and accurate diagnostic analysis.

10. The diagnostic device (10) of claim 1, further comprising: a haptic feedback system integrated into the main body (12) to provide tactile guidance to the operator during the maneuvering of the device (10); wherein the processor (302) generates haptic feedback patterns based on the realtime analysis of collected data and the current phase of the diagnostic process; and wherein the haptic feedback system works in conjunction with the audio and visual instructions to enhance the operator's ability to accurately position and move the device (10) during both the first and second scans.

11. The diagnostic device (10) of claim 1, wherein the AI / ML engine (310) is configured to: continuously learn and improve its performance based on accumulated data from multiple diagnostic sessions; adapt its algorithms to account for variations in patient physiology, environmental conditions, and operator skill levels; generate personalized diagnostic protocols based on patient-specific factors identified in the preliminary inputs and during the diagnostic process; and provide explanations for its decisions and recommendations to enhance transparency and assist in operator training.

12. The diagnostic device (10) of claim 1, wherein:the user output modules (22) comprise a high-resolution touchscreen display and spatial audio speakers; the touchscreen display is configured to show real-time visualizations of the scanned areas, highlighting potential abnormalities and providing intuitive controls for the operator to interact with the device (10); and the spatial audio speakers are designed to provide directional audio cues to guide the operator's hand movements during the scanning process, enhancing the precision of device positioning without requiring the operator to constantly look at the visual display.

13. A method for guiding an operator in performing diagnostic procedures on a patient using a diagnostic device (10), the method comprising: placing (402) a bottom surface (14) of the diagnostic device (10) in contact with a patient's body, wherein the diagnostic device (10) comprises a set of peripheral sensors (16) and diagnostic sensors (18) arranged on the bottom surface (14); receiving (404) and processing, by a preliminary data acquisition module (307), preliminary inputs comprising patient medical history and symptoms; determining (406) by an artificial intelligence / machine learning (AI / ML) engine (310), a target body region and a starting point to initiate diagnosis based on the preliminary inputs; performing (408) a first scan of the target body region for determining boundaries of the target body region by: generating, by the AI / ML engine (310), a first set of audio or visual instructions to perform the first scan after placing the bottom surface (14) over the starting point, capturing (410), by the peripheral sensors (16), a first data stream in realtime during the first scan, processing (412), by the AI / ML engine (310), the first data stream to generate feedback for the operator and record boundaries of the target body region; performing (414) a second scan of a subset of organs within the target body region after repositioning the diagnostic device (10) at the starting point by iteratively examining each organ in the subset of organs, wherein examining each organ comprises: generating (416), by the AI / ML engine (310), a second set of instructions to perform the second scan based on the recorded boundaries and preliminary inputs, receiving (418), a second data stream from the peripheral sensors (16) and a third data stream from the diagnostic sensors (18),processing (420), by the AI / ML engine (310), the second and third data streams to generate a third set of real-time instructions for conducting diagnostics of the organ, receiving (422), feedback from the patient regarding pain or discomfort, processing (424), by the AI / ML engine (310), the second data stream, third data stream, and patient feedback to identify abnormalities associated with the organ and determine a successive organ to be examined, generating (426), a fourth set of real-time instructions for navigating the device (10) to the successive organ; processing (428), by the AI / ML engine (310), all collected data streams and patient feedback to: generate preliminary diagnoses with confidence levels, suggest additional tests when confidence levels fall below a predetermined threshold, provide treatment recommendations, and identify emergency care requirements; transmitting (430), via a connection module (312), the collected diagnostic data to: connected medical devices, electronic health record systems, the operator's personal storage device, and a central data repository; and displaying (432), through user output modules (22), diagnostic results comprising: visualizations of scanned areas, identified abnormalities, recommended treatment options, nearby medical facilities, and available healthcare resources.Dated this 14thday of June 2024

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