Apparatus, system and method for analysis of biological samples

Abstract

Disclosed a system for analyzing biological samples that includes an information processing apparatus that further includes a processing circuitry The processing circuitry is configured to receive one or more images of a biological sample and generate a whole slide image such that the generation of the whole slide image includes detect one or more features in one or more images, calculate a homography matrix based on the one or more detected features and stitch the one or more images together to generate the whole slide image. Detect, by way of a detector module, one or more features in the whole slide image, classify, one or more detected features based on morphological data, one or and morphological defects by a first classifier and second classifier respectively, and generate a report. Further, a method for analyzing biological samples is disclosed.

Description

[0001] APPARATUS, SYSTEM AND METHOD FOR ANALYSIS OF BIOLOGICAL SAMPLES

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to biological sample analysis. More particularly, the disclosure relates to an information processing apparatus, system and a method for analysis of biological samples.

[0004] BACKGROUND

[0005] Routine microscopy remains heavily dependent on the expertise of pathologists, as accurate diagnosis and interpretation of biological samples require specialized knowledge and experience. While certain devices, such as hematology analyzers, have been developed to assist in reducing this reliance, they still require expert interpretation. These devices typically provide around multiple parameters related to blood and its morphology; however, when abnormalities are found, a blood smear must still be manually prepared and examined by a pathologist. The reliance on human expertise continues to create challenges in achieving timely and efficient diagnoses.

[0006] To address this issue, some conventional devices have incorporated computer vision-based diagnostics to automate the interpretation of blood smears, offering morphological analysis. However, these devices are often prohibitively expensive, which makes them inaccessible to most standalone and mid-scale labs. These high costs limit their use to large diagnostic chains and central hubs, leaving smaller facilities without access to advanced diagnostic tools. Furthermore, the scope of these devices is often restricted to a single sample type, such as blood, with only a few devices expanding to include urine in addition to blood, further limiting their utility.

[0007] The high cost of these devices is largely driven by the significant development expenses associated with the Artificial Intelligence (Al) models they rely on. These Al models are typically based on supervised learning, which requires vast amounts of labeled data for accurate predictions. For instance, blood cell or hematology analyzers, which are crucial medical devices used to count and classify blood cells (such as red blood cells, white blood cells, and platelets), rely on the Coulter Counter Method. This well-established technique uses electrical impedance to measure the size and number of blood cells by analyzing disruptions in an electrical current as blood cells pass through an aperture. While these analyzers provide critical diagnostic data, including cell counts, size distributions, and graphical representations, they too come with limitations. The Coulter Counter Method requires the use of a large amount of proprietary reagent, frequent calibration, and regular maintenance, which adds to the overall cost and complexity of the system.

[0008] Moreover, imaging-based methods for sample analysis, such as those used for cell counting, are not immune to issues either. In these systems, a microscope objective is used to observe a prepared sample as it moves through an observation zone, with manual analysis following the imaging process. This method is prone to manual errors, leading to potential misinterpretations and wrong analysis. Additionally, manual analysis is time-consuming, delaying critical test results and often requiring a skilled and trained workforce. This contributes to labor intensiveness and increased costs for healthcare facilities.

[0009] Therefore, there is a need for an apparatus, system and method to overcome the limitations of the as of now available methods for real-time analysis of one or more biological samples.

[0010] SUMMARY

[0011] In an aspect of the present disclosure, an information processing apparatus is disclosed. The information processing apparatus includes processing circuitry coupled to an analyzer. The processing circuitry is configured to receive one or more images of a biological sample, generate a whole slide image of the biological sample from the one or more images, such that the generation of the whole slide image includes detect one or more features in one or more images, calculate a homography matrix based on the one or more detected features; and stitch the one or more images together based on the homography matrix to generate the whole slide image. The processing circuitry further configured to detect, by way of a detector module, one or more features in the whole slide image, classify, by way of a first classifier, one or more detected features based on morphological data, classify, by way of a second classifier, one or more detected features based on morphological defects, and generate a report including i) one or more features classified by the first classifier; and ii) one or more features classified by the second classifier.

[0012] In some aspects of the present disclosure, the biological sample is selected from a group including blood, sweat, urine, blood serum, semen, breast milk, saliva, blood plasma, tears, mucus, cerebrospinal fluids, amniotic fluid, vaginal lubrication fluids, pus, lymph, bile, synovial fluid, aqueous humour, phlegm, gastric acid, preejaculate, or colostrum.

[0013] In some aspects of the present disclosure, the one or more features are selected from a group including at least one of, cell counts, cell types, complex cells, cell morphology, or presence of abnormal structures or structure specific to biological sample.

[0014] In some aspects of the present disclosure, the detector module is selected from a group including, but not limited to, a SIFT, ORB, SuperPoint or Optical Flow based Method like FlowNet or LiteFlowNet.

[0015] In some aspects of the present disclosure, the first classifier is selected from a group including, but not limited to, a YOLO, RCNN, AlexNet or Grounding Foundation Model such SAM, Florence 2b, Grounding Dino, and Grounding SAM.

[0016] In some aspects of the present disclosure, the second classifier includes a Multi Modal Large Language Model.

[0017] In some aspects of the present disclosure, the morphological defects are selected from a group including anisocytosis, poikilocytosis, hypochromia, hyperchromia, polychromasia, Howell-Jolly bodies, basophilic stippling, Pappenheimer bodies, Heinz bodies, nucleated red blood cells, hypersegmented neutrophils, hyposegmented neutrophils, toxic granulation, Dohle bodies, vacuolation, Auer rods, atypical lymphocytes or blast cells. In some aspects of the present disclosure, the processing circuitry is further configured to: a. displays the first generated report to a user, b. receive input from the user; and c. generate the report based on the first report and the input received from the user.

[0018] In an aspect of the present disclosure, a system for analyzing biological samples. The system includes an analyzer configured to capture one or more images of the biological sample. The system further includes an information processing apparatus. The information processing apparatus includes processing circuitry coupled to an analyzer. The processing circuitry is configured to receive one or more images of a biological sample, generate a whole slide image of the biological sample from the one or more images, such that the generation of the whole slide image includes detect one or more features in one or more images, calculate a homography matrix based on the one or more detected features; and stitch the one or more images together based on the homography matrix to generate the whole slide image. The processing circuitry further configured to detect, by way of a detector module, one or more features in the whole slide image, classify, by way of a first classifier, one or more detected features based on morphological data, classify, by way of a second classifier, one or more detected features based on morphological defects, and generate a report including i) one or more features classified by the first classifier; and ii) one or more features classified by the second classifier.

[0019] In some aspects of the present disclosure, the biological sample is selected from a group includes blood, sweat, urine, blood serum, semen, breast milk, saliva, blood plasma, tears, mucus, cerebrospinal fluids, amniotic fluid, vaginal lubrication fluids, pus, lymph, bile, synovial fluid, aqueous humour, phlegm, gastric acid, preejaculate, or colostrum.

[0020] In some aspects of the present disclosure, the one or more features are selected from a group including at least one of, cell counts, cell types, complex cells, cell morphology, or presence of abnormal structures or structure specific to biological sample. In some aspects of the present disclosure, the detector module is selected from a group including, but not limited to, a SIFT, ORB, SuperPoint or Optical Flow based Method like FlowNet or LiteFlowNet.

[0021] In some aspects of the present disclosure, the first classifier is selected from a group including, but not limited to, a YOLO, RCNN, AlexNet or Grounding Foundation Model such as SAM, Florence 2b, Grounding Dino, and Grounding SAM.

[0022] In some aspects of the present disclosure, the second classifier includes a Multi Modal Large Language Model.

[0023] In some aspects of the present disclosure, the morphological defects are selected from a group including anisocytosis, poikilocytosis, hypochromia, hyperchromia, polychromasia, Howell-Jolly bodies, basophilic stippling, Pappenheimer bodies, Heinz bodies, nucleated red blood cells, hypersegmented neutrophils, hyposegmented neutrophils, toxic granulation, Dohle bodies, vacuolation, Auer rods, atypical lymphocytes or blast cells.

[0024] In some aspects of the present disclosure, the processing circuitry is further configured to display the generated report to a user, receive input from the user, and generate the report based on the report and the input received from the user.

[0025] In an aspect of the present disclosure, a method for analyzing biological samples is disclosed. The method includes receiving, by way of processing circuitry, one or more images of a biological sample, followed by generating, by way of processing circuitry, a whole slide image of the biological sample from the one or more images. The step generating the whole slide image includes detecting one or more features in one or more images, calculating a homography matrix based on the one or more detected features, and stitching the one or more image frames together based on the homography matrix to generate the whole slide image. The method further includes detecting, by way of a detector module, one or more features in the whole slide image, followed classifying, by way of a first classifier, one or more detected features based on morphological data, classifying, by way of a second classifier, one or more detected features based on morphological defects, and generating a report including- i) one or more features classified by the first classifier; and ii) one or more features classified by the second classifier.

[0026] In some aspects of the present disclosure, the method further includes displaying the generated report to a user, receiving input from the user; and generating the report based on the displayed report and the input received from the user.

[0027] BRIEF DESCRIPTION OF DRAWINGS

[0028] The above and still further features and advantages of aspects of the present disclosure becomes apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:

[0029] FIG. 1 is a block diagram that illustrates a system for analysis of a biological sample, in accordance with an aspect of the present disclosure;

[0030] FIG. 2 is a block diagram that illustrates an information processing apparatus of the system, in accordance with an aspect of the present disclosure; and

[0031] FIG. 3 illustrates a flowchart of a method for analysis of a biological sample, in accordance with an aspect of the present disclosure.

[0032] To facilitate understanding, reference numerals have been used, where possible, to designate elements common to the figures.

[0033] DETAILED DESCRIPTION

[0034] Various aspects of the present disclosure provide an information processing apparatus for analysis of a biological sample, system and a method thereof. The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.

[0035] The various aspects including the example aspects are now described in detail with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.

[0036] It is understood that when an element or layer is referred to as being “on,” “connected to,” or “coupled to” another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0037] The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventor / inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various aspects including the example aspects relate to a system for analysis of a biological sample and a method thereof. The present disclosure relates to an information processing apparatus, system and method for analysis of the biological sample. More particularly, the present disclosure provides the information processing apparatus that may be configured to analyze one or more images of the biological sample and generate a report based on that analysis by way of an artificial intelligence model.

[0038] The information processing apparatus is configured to analyze one or more images of the biological sample. The information processing apparatus may include the processing circuitry configured to receive one or more whole slide images of the biological sample and further divide the one or more whole slide images into tiles to detect one or more objects and classify plurality of features of the biological sample. Further, the processing circuitry of the information processing apparatus may generate the report based on the detected one or more object and the plurality of classified features in the whole slide images of the biological sample by way of the artificial intelligence model.

[0039] Various aspects and embodiments of the system for analysis of the biological sample and its components will be described in further detail in the following sections. It should be understood that the described aspects and embodiments are exemplary in nature and that modifications, combinations, and variations are possible within the scope of the present disclosure.

[0040] As used herein, the term “morphological data” refers to the information collected about the shape, size, structure, and overall appearance of a cell.

[0041] As used herein, the term “morphological defects” refers to the defects in the cell that includes abnormalities or irregularities in the structure and shape of the cell.

[0042] FIG. 1 illustrates a system for analysis of the biological sample, in accordance with an aspect of the present disclosure. The system 100 may include an analyzer 102, an information processing apparatus 104, and a user device 106 communicatively coupled with each other. The analyzer 102 may be configured to capture one or more images of the biological sample by way of an imaging unit 108. The analyzer 102 may further include a memory unit 110 and a first communication interface 112.

[0043] In some aspects, the imaging unit 108 may capture one or more images of the biological sample. In some aspects, the biological sample may be selected from a group including, but not limited to, blood, sweat, urine, blood serum, semen, breast milk, saliva, blood plasma, tears, mucus, cerebrospinal fluids, amniotic fluid, vaginal lubrication fluids, pus, lymph, bile, synovial fluid, aqueous humour, phlegm, gastric acid, pre-ejaculate, or colostrum. Aspects of the present disclosure are intended to include or otherwise cover any type of biological sample including known, related art, and / or later developed biological samples.

[0044] In some aspects of the present disclosure, the imaging unit 108 may be configured to capture an image or a video by way of a camera. The camera may be configured to capture one or more frames of image or video of the biological sample. Examples of the camera may include but are not limited to, Raspberry Pi Global Shutter Camera, See3CAM_50CUG_CXLCC_BX_H01Rl camera, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of camera including known, related art, and / or later developed camera for imaging unit 108.

[0045] In some aspects of the present disclosure, the specification of the Raspberry Pi Global Shutter Camera may include Sony IMX296LQR-C sensor, 1.58-megapixels resolution, 6.3mm sensor size, and 3.45pm x 3.45pm pixel size. In another aspect of the present disclosure, the specification of the See3CAM_50CUG_CXLCC_B X_H01Rl camera may include IMX264 from Sony® Pregius sensor, 5-megapixel resolution, 2 / 3 inches sensor, 3.45 pm x 3.45 pm pixel size. Aspects of the present disclosure are intended to include or otherwise cover any type of Raspberry Pi Global Shutter Camera including known, related art, and / or later developed Raspberry Pi Global Shutter Camera.

[0046] In an aspect of the present invention, the memory unit 110 may be configured to save the images or video frames captured by the camera. The memory unit 110 may be configured to store the logic, instructions, circuitry, interfaces and / or codes and data associated with the analyzer 102. Examples of the memory unit 110 may include but are not limited to, Read-Only Memory (ROM), Random-Access Memory (RAM), flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM) and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of memory unit 110 including known, related art, and / or later developed memories.

[0047] In some aspects of the present disclosure, the first communication interface 112 may be configured to enable the analyzer 102 to communicate with other parts of the system 100. Examples of the first communication interface 112 may include but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, a local buffer circuit or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of first communication interface 112 including known, related art, and / or later developed communication interface. In another embodiment, the first communication interface 112 may include any device and / or apparatus capable of providing wireless or wired communications between the other parts of the system 100.

[0048] The information processing apparatus 104 of the system 100 may include a processing circuitry 114, a second memory unit 116, a database 118, and a second communication interface 120.

[0049] In some aspects of the present disclosure, the information processing apparatus 104 may be a network of computers, a framework, or a combination thereof, that may provide a generalized approach to create a server implementation. In some aspects of the present disclosure, the information processing apparatus 104 may be a server. Examples of the information processing apparatus 104 may include but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The information processing apparatus 104 may be realized through various webbased technologies such as, but not limited to, a Java web framework, a .NET framework, a personal home page (PHP) framework, JavaScript, React, Python, PyQt, Qt, and C / C++ or any other web application framework. Aspects of the present disclosure are intended to include or otherwise cover any type of web-based technologies including known, related art, and / or later developed web-based technologies.

[0050] The processing circuitry 114 of the information processing apparatus 104 may be configured to receive one or more images and the videos captured by the imaging unit 108 of the analyzer 102 by way of the first communication interface 112 of the analyzer 102. Examples of the processing circuitry 114 may include but are not limited to, Raspberry Pi 5, Nvidia Jetson Nano, Nvidia Orin Nano, Nvidia Xavier NX, or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of processing circuitry 114, without deviating from the scope of the present disclosure.

[0051] In some aspects of the present disclosure, the specification of the Raspberry Pi 5 may be powered by a Broadcom BCM2712, a 2.4GHz quad-core 64-bit Arm Cortex- A76 CPU with cryptography extensions, 512KB per-core L2 caches, and a 2MB shared L3 cache. A VideoCore VII GPU may be featured, supporting OpenGL ES 3.1 and Vulkan 1.2 for advanced graphics capabilities. The display output may include dual 4Kp60 HDMI with HDR support, enabling high-quality visuals. For video decoding, a 4Kp60 HEVC decoder may be supported for smooth playback of high-resolution content. Memory options may include LPDDR4X-4267 SDRAM, available in 2GB, 4GB, or 8GB configurations. Networking may be handled by dual-band 802.1 lac Wi-Fi, Bluetooth 5.0 / BUE, and Gigabit Ethernet, with PoE+ support for power over Ethernet. Storage may be provided via a microSD card slot that supports high-speed SDR104 mode for fast data transfer. Two USB 3.0 ports (5Gbps) and two USB 2.0 ports may be available for peripheral connectivity, along with a camera interface supporting 2 x 4-lane MIPI camera / display transceivers. A PCIe 2.0 xl interface may be included for fast peripherals, requiring an M.2 HAT or adapter for expansion. Power may be supplied through a 5V / 5A DC input via USB-C, with Power Delivery support. Further I / O may include a 40-pin header, a real-time clock (RTC), and a power button for easy operation.

[0052] In other aspects of the present disclosure, the specification of the Nvidia Jetson Nano may be powered by a quad-core ARM Cortex-A57 MPCore processor for efficient multi-core performance. Further, an NVIDIA Maxwell architecture GPU with 128 CUDA cores, providing robust graphics and parallel processing capabilities. Display outputs may include HDMI 2.0 and eDP 1.4 for flexible connectivity options. For video encoding, the system may support 250MP / sec, with capabilities including lx 4K @ 30 (HEVC), 2x 1080p @ 60 (HEVC), 4x 1080p @ 30 (HEVC), and 4x 720p @ 60 (HEVC). Video decoding may reach 500MP / sec, supporting lx 4K @ 60 (HEVC), 2x 4K @ 30 (HEVC), 4x 1080p @ 60 (HEVC), 8x 1080p @ 30 (HEVC), and 9x 720p @ 60 (HEVC). Memory may include 4GB of 64-bit LPDDR4, operating at 1600MHz with a bandwidth of 25.6 GB / s. Networking may be supported by Gigabit Ethernet for high-speed connectivity. Storage may include 16GB of eMMC 5.1 for fast and reliable data storage. USB connectivity may offer 4x USB 3.0 ports and a USB 2.0 Micro-B port for peripheral support. The camera interface may support 12 lanes (3x4 or 4x2) MIPI CSI-2 D- PHY 1.1, with a data rate of 1.5Gb / s per pair. A PCIe M.2 Key E slot may be included for fast peripheral expansion. Power may be supplied via 5V DC through Micro-USB or the GPIO header. Further I / O options may include GPIO, I2C, I2S, SPI, and UART for various peripheral connections and control functionalities.

[0053] In other aspects of the present disclosure, the specification of the Nvidia Jetson Orin Nano may be powered by a 6-core Arm Cortex-A78AE v8.2 64-bit CPU with 1.5MB of L2 cache and 4MB of L3 cache for efficient processing and performance. Further, a 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores, enabling advanced graphics and Al processing capabilities. Display outputs may include lx 4K30 multi-mode DP 1.2 (+MST), eDP 1.4, and HDMI 1.4 for versatile connectivity options. Video encoding may support 1080p30, utilizing 1-2 CPU cores, while video decoding may support lx 4K60 (H.265), 2x 4K30 (H.265), 5x 1080p60 (H.265), and l lx 1080p30 (H.265) for smooth, high-quality playback. Memory may include 8GB of 64-bit LPDDR5, offering a high bandwidth of 68GB / s for demanding applications. Networking may be supported by Gigabit Ethernet for fast and reliable internet connectivity. The system may support external NVMe storage for expanded data capacity and faster access speeds. USB connectivity may include 3x USB 3.2 Gen2 ports (lOGbps) and 3x USB 2.0 ports for a wide range of peripheral connections. The camera interface may support up to 4 cameras (8 via virtual channels) with 8 MIPI CSI-2 lanes and D-PHY 2.1, offering data rates of up to 20Gbps. PCIe may feature 1x4 and 3x1 PCIe Gen3 ports for high-speed peripheral expansion. Power consumption may range from 5-15W, depending on configuration. Further I / O options may include UART, SPI, I2C, I2S, GPIOs, CAN, and PWM for additional connectivity and control.

[0054] In other aspects of the present disclosure, the specification of the Nvidia Xavier NX may be powered by a 6-core NVIDIA Carmel ARM v8.2 64-bit CPU with 6MB of L2 cache and 4MB of L3 cache for high-performance processing. Further, a 384- core NVIDIA Volta GPU with 48 Tensor Cores, enabling powerful graphics and Al-driven workloads. Display outputs may include 2x multi-mode DP 1.4, eDP 1.4, and HDMI 2.0, offering versatile connectivity for a range of display devices. Video encoding may support 2x 4K60, 4x 4K30, lOx 1080p60, and 22x 1080p30 (H.265), while video decoding may support 2x 8K3O, 6x 4K60, 12x 4K30, 22x 1080p60, and 44x 1080p30 (H.265) for exceptional media playback capabilities. Memory options may include 16GB or 8GB of 128-bit LPDDR4x, providing 59.7GB / s of bandwidth for demanding applications. Networking may be supported by Gigabit Ethernet for reliable and fast data transfer. Storage may include 16GB of eMMC 5.1 for quick and reliable onboard storage. USB connectivity may offer lx USB 3.1, 4x USB 3.0, and 4x USB 2.0 ports for diverse peripheral support. The camera interface may support up to 6 cameras (24 via virtual channels) with 14 MIPI CSI- 2 lanes and D-PHY 1.2, offering data rates of up to 30Gbps. PCIe may include 1x1 PCIe Gen3 and 1x4 PCIe Gen4 slots for high-speed peripheral expansion. Power consumption may range from 10W to 20W, depending on the configuration. Further I / O options may include 2x NVDLA Engines, GPIOs, SPI, I2C, and UART for additional connectivity and control features.

[0055] In some aspects, the processing circuitry 110 may be configured to generate a whole slide image based on the one or more images and / or videos received from the analyzer 102. The processing circuitry may divide the received one or more images and / or videos to one or more tiles. The processing circuitry 114 may further be configured to shuffle the one or more tiles of one or more images and / or videos and may determine a spatial relationship, contextual information, and an intrinsic structure between each part of one or more images and / or videos using one or more computer vision models. For example, one or more computer vision model may include, but not limited to, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Natural Language Processing (NLP), Text Analysis, Speech Recognition, Image Recognition, Object Detection, Expert Systems, Rule-Based Systems, Genetic Algorithms, Evolutionary Algorithms, Robotics, Robotic Process Automation (RPA), Autonomous Robots, Knowledge Representation and Reasoning, Ontologies, Clustering Algorithms (e.g., K-Means, Hierarchical Clustering), Recommender Systems, Collaborative Filtering, Content- Based Filtering, Fuzzy Logic, Fuzzy Systems, Monte Carlo Tree Search, Swarm Intelligence, Ant Colony Optimization, Particle Swarm Optimization, Anomaly Detection, Multi Modal Large Language Model or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of computer vision model, without deviating from the scope of the present disclosure. The computer vision model for object detection may be working on YOLO, RCNN, AlexNet or Grounding Foundation Model such SAM, Florence 2b, Grounding Dino, Grounding SAM, or the like. Aspects of the present disclosure are intended to include and / or otherwise cover working of any computer vision model that may be suitable for the detection of one or more features of the biological sample, without deviating from the scope of the present disclosure.

[0056] In some aspects, the processing circuitry 110 may include a feature detector module (not shown), a feature matching module (not shown), a homography matrix calculator (not shown), a canvas generator (not shown), a stitching module (not shown), and a blending module (not shown). The feature detector module may be configured to detect one or more features of the biological sample in the one or more tiles of the one or more images and / or videos. Examples of the feature detector module may include, but are not limited to, SIFT, ORB, SuperPoint, Optical Flow based Method like Flownet or LiteFlowNet, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of feature detection module including known, related art, and / or later developed feature detection module. In some aspects, the one or more features may be selected from a group including, but not limited to, cell counts, cell types, cell morphology, or presence of abnormal cells or structures specific to the biological sample type or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of features including known, related art, and / or later developed features, without deviating from the scope of the present disclosure. Based on the detected one or more features, the feature matching module may be configured to compare the one or more detected features of the biological sample with the pre-trained data of the biological sample stored in the database. Examples of the feature matching module may include, but are not limited to, Brute Force Matcher, FLANN Based Matcher, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of feature matching module including known, related art, and / or later developed feature detection module. Further, the homography matrix calculator may be configured to calculate the homography matrix of the biological sample based on the one or more matched features or Optical Flow of the optical flow model of the detected feature. Further, the canvas generator module is configured to generate a canvas by way of the homography matrix calculated by the homography matrix calculator. Further, the stitching module is configured to stitch the frames generated by the canvas generator and further blending modules are configured to blend the stitched whole slide image of the biological sample.

[0057] In some aspects of the present disclosure, the second memory unit 116 may be configured to save the image or video frame captured by the camera. The second memory unit 116 may be configured to store the logic, instructions, circuitry, interfaces, checkpoints, formats of the data, cache, monitoring and debugging data, and / or codes and data associated with the analyzer 102. Examples of the second memory unit 116 may include but are not limited to, Read-Only Memory (ROM), Random-Access Memory (RAM), flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM) or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of second memory unit 116 including known, related art, and / or later developed memories.

[0058] In some aspects of the present disclosure, the database 118 may be configured to store the logic, instructions, circuitry, interfaces, and / or algorithms of the processing circuitry 114 for executing various operations. The database 118 may be configured to store therein data associated with users registered within the system 100. The database 118 may be configured to store one or more whole slide image of the biological sample divided into one or more tiles and / or one or more computer vision model that may be used for the generation of the classification feature and / or detection of the one or more objects in the whole slide images of the biological sample. In some embodiments of the present disclosure, the database 118 may further be configured to store the data or the representations of the state of the user that may be generated by the processing circuitry 114. Examples of the database 118 may include but are not limited to, a ROM, a RAM, a flash memory, a removable storage drive, a HDD, a solid-state memory, a magnetic storage drive, a PROM, an EPROM, and / or an EEPROM or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the database 118 including known, related art, and / or later developed database, without deviating from the scope of the present disclosure.

[0059] In some aspects of the present disclosure, the second communication interface 120 may be configured to enable the processing circuitry 114 to communicate with other parts of the system 100. Examples of the second communication interface 120 may include but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, a local buffer circuit, a Local Area Networks (LAN), a Wide Area Networks (WAN), a Wireless Local Area Network (WLAN) or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the second communication interface 120 including known, related art, and / or later developed second communication interface 120. It will be apparent to a person of ordinary skill in the art that the second communication interface 120 includes any device and / or apparatus capable of providing wireless or wired communications between the other parts of the system 100.

[0060] In some aspects of the present disclosure, the user device 106 may include a user interface 122, a device processing unit 124, a third memory unit 126, a detection console 128, and a third communication interface 130. In some aspects of the present disclosure, the user device 106 may be configured to receive one or more results of the one or more whole slide images of the biological sample. The user device 106 may be configured to receive the result of the state of the user from the processing circuitry 114 of the information processing apparatus 104, which may be in the form of at least one of, a report, a pictorial representation, a graph, a text, a voice output, or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of result of the state of the user including known and / or related, or later developed representations.

[0061] In some aspects of the present disclosure, the user interface 122 may include an input interface (not shown) for receiving inputs from the user. Examples of the input interface of the user interface 122 may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and / or later developed technologies. The user interface 122 may include an output interface (not shown) for displaying (or presenting) an output to the user. Examples of the output interface of the user interface 122 may include, but are not limited to, a digital display, an analogue display, a touch screen display, a graphical user interface, a website, a web page, a keyboard, a mouse, a light pen, an appearance of a desktop, illuminated characters or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of the output interface including known and / or related, or later developed technologies.

[0062] In some aspects of the present disclosure, the device processing unit 124 may include suitable logic, instructions, circuitry, interfaces, and / or codes for executing various operations, such as the operations associated with the user device 106, and the like. In some aspects of the present disclosure, the device processing unit 124 may utilize one or more processors such as Arduino or Raspberry Pi, Nvidia Jetson Series or any similar embedded processors i.e., based on an ASIC processor, a RISC processor, a CISC processor, an FPGA, ARM Architecture Processor, X86 Architecture Processor, any NVIDIA GPU Architecture or any other GPU, or any cloud infrastructures or similar platform or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the processor including known, related art, and / or later developed processors. Further, the device processing unit 124 may be configured to control one or more operations executed by the user device 106 in response to the input received at the user interface 122 from the user. Examples of the device processing unit 124 may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of processing unit including known, related art, and / or later developed processing units.

[0063] In some aspects of the present disclosure, the third memory unit 126 may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the device processing unit 124, data associated with the user device 106, and data associated with the system 100. Examples of the third memory unit 126 may include but are not limited to, Read-Only Memory (ROM), Random-Access Memory (RAM), flash memory, a removable storage drive, a hard disk drive (HDD), solid- state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM) or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of third memory including known, related art, and / or later developed memories.

[0064] In some aspects of the present disclosure, the detection console 128 may be configured as a computer-executable application, to be executed by the device processing unit 124. The detection console 128 may include suitable logic, instructions, and / or codes for executing various operations. One or more computerexecutable applications may be stored in the third memory unit 126. Examples of one or more computer-executable applications may include, but are not limited to, an audio application, a video application, a social media application, a navigation application, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of computer-executable application including known, related art, and / or later developed computer-executable applications.

[0065] In some aspects of the present disclosure, the third communication interface 130 may be configured to enable the user device 106 to communicate with other parts of the system 100. Examples of the third communication interface 130 may include but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, a local buffer circuit or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of third communication interface including known, related art, and / or later developed third communication interface. It will be apparent to a person of ordinary skill in the art that the third communication interface 130 may include any device and / or apparatus capable of providing wireless or wired communications between the other parts of the system 100.

[0066] In some embodiments of the present disclosure, the communication network (not shown) may include suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for the transmission and reception of data related to operations of various entities of the system 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The communication network may be associated with an application layer for the implementation of communication protocols based on one or more communication requests from the user device 106 and the processing circuitry 114 of the information processing apparatus 104. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication protocols including known, related art, and / or later developed communication protocols.

[0067] In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of a plurality of communication channels in the communication network. The communication channels may include but are not limited to, a wireless channel, a wired channel, or a combination of wireless and wired channels thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, a fibre optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and / or later developed technologies.

[0068] FIG. 2 illustrates a block diagram that illustrates the information processing apparatus 104 of system 100, in accordance with an exemplary aspect of the present disclosure.

[0069] In some aspects of the present disclosure, the information processing apparatus 104 may include the processing circuitry 114 and the database 118. Further, the information processing apparatus 104 may include a network interface 200 and an input / output (I / O) interface 202. The processing circuitry 114, the database 118, the network interface 200, and the I / O interface 202 may communicate with each other by way of a first communication bus 222. It will be apparent to a person with ordinary art skill having ordinary skill in the art that the information processing apparatus 104 is for illustrative purposes and not limited to any specific combination of hardware circuitry and / or software.

[0070] The processing circuitry 114 may be configured to execute various operations associated with the system 100. Specifically, the processing circuitry 114 may be configured to execute the one or more operations associated with the system 100 by communicating one or more commands and / or instructions over the communication network 118 to the user device 106 and the information processing apparatus 104. Examples of the processing circuitry 200 may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), or the like. Embodiments of the present disclosure are intended to include and / or otherwise cover any type of the processing circuitry 114 including known, related art, and / or later developed technologies.

[0071] The database 118 may be configured to store logic, instructions, circuitry, interfaces, and / or codes of the processing circuitry 114 to enable the processing circuitry 114 to execute the one or more operations associated with the system 100. The database 118 may be further configured to store therein data associated with the system 100, and the like. It will be apparent to a person having ordinary skill in the art that the database 118 may be configured to store various types of data associated with the system 100, without deviating from the scope of the present disclosure. Examples of the database 118 may include but are not limited to, a Relational database, a NoSQL database, a Cloud database, an Object-oriented database, and the like. Further, the database 118 may include associated memories that may include, but is not limited to, a Read-Only Memory (ROM), a Random Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM) or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the database 118 including known, related art, and / or later developed technologies. In some embodiments of the present disclosure, a set of centralized or distributed networks of peripheral memory devices may be interfaced with the information processing apparatus 104, as an example, on a cloud server.

[0072] In some aspects of the present disclosure, the network interface 200 may include suitable logic, circuitry, and interfaces that may be configured to establish and enable communication between the information processing apparatus 104 and different components of the system 100 via the communication network 118. The network interface 200 may be implemented by use of various known technologies to support wired or wireless communication of the information processing apparatus 104 with the communication network 118. The network interface 200 may include but is not limited to, an antenna, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a SIM card, and a local buffer circuit or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of network interface including known, related art, and / or later developed network interface.

[0073] In some aspects of the present disclosure, the VO interface 202 may include suitable logic, circuitry, interfaces, and / or code that may be configured to receive inputs and transmit server outputs via a plurality of data ports in the information processing apparatus 104. The VO interface 202 may include various input and output data ports for different VO devices. Examples of such VO devices may include but are not limited to, a touch screen, a keyboard, a mouse, a joystick, a projector audio output, a microphone, an image-capture device, a liquid crystal display (LCD) screen and / or a speaker or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of interface including known, related art, and / or later developed interface.

[0074] In some aspects of the present disclosure, the processing circuitry 114 may include a query engine 204, a registration engine 206, a data collection engine 208, a data processing engine 210, detector module, a first classifier 214, a second classifier, a report generation engine 218 and a display engine 220. The query engine 204, registration engine 206, data collection engine 208, a data processing engine 210, detector module 212, first classifier 214, second classifier 216, report generation engine 218 and the display engine 220 may communicate with each other by way of a second communication bus 224. It will be apparent to a person having ordinary skill in the art that the information processing apparatus 104 is for illustrative purposes and not limited to any specific combination of hardware circuitry and / or software.

[0075] In some aspects of the present disclosure, the query engine 204 may be configured to enable the user to input a query by way of the user device 106 into the system 100 by providing query data through a query menu (not shown) of the detection console 128 displayed through the user device 106. The query data may include, but is not limited to, some personal details of the user such as name, age, verification ID, email ID, medical history of the patient, or the like. In some aspects of the present disclosure, the query data may further include registration photographs of the user. The query engine 204 can be configured to utilize the details provided by the user and further classify based on the details provided.

[0076] In some aspects of the present disclosure, the registration engine 206 may be configured to enable the user to register into the system 100 by providing registration data through a registration menu. The registration data may include, but is not limited to, a name, a demographics, a contact number, an address, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of registration data. In some aspects, the registration engine 206 may be further configured to enable the user to create a login identifier and a password that may enable the user to subsequently log into the system 100. The registration engine 206 may be configured to store the registration data associated with the first and second users, the login and the password associated with the first and second users in a LookUp Table (LUT) (not shown) provided in the database 118.

[0077] In some aspects of the present disclosure, the data collection engine 208 may be configured to receive one or more images and / or videos from the imaging unit 108 of the analyzer 102. The data collection engine 208 may store one or more images and / or videos in the database 118.

[0078] The data processing engine 210 may be configured to generate the whole slide image or video from the data collection engine 208. In some aspects, the data processing engine 210 may be configured to generate a whole slide image based on the one or more images and / or videos received from the analyzer 102. The data processing engine 210 may divide the received one or more images and / or videos to one or more tiles. The data processing engine 210 may further be configured to shuffle the one or more tiles of one or more images and / or videos and may determine a spatial relationship, contextual information, and an intrinsic structure between each part of one or more images and / or videos using a computer vision model. For example, one or more computer vision model may include, but not limited to, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Natural Language Processing (NLP), Text Analysis, Speech Recognition, Image Recognition, Object Detection, Expert Systems, Rule-Based Systems, Genetic Algorithms, Evolutionary Algorithms, Robotics, Robotic Process Automation (RPA), Autonomous Robots, Knowledge Representation and Reasoning, Ontologies, Clustering Algorithms (e.g., K-Means, Hierarchical Clustering), Recommender Systems, Collaborative Filtering, Content-Based Filtering, Fuzzy Logic, Fuzzy Systems, Monte Carlo Tree Search, Swarm Intelligence, Ant Colony Optimization, Particle Swarm Optimization, Anomaly Detection, Multi Modal Large Language Model or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of computer vision model, without deviating from the scope of the present disclosure. The computer vision model for object detection may be working on YOLO, RCNN, AlexNet or Grounding Foundation Model such as SAM, Florence 2b, Grounding Dino, Grounding SAM, or the like. Aspects of the present disclosure are intended to include and / or otherwise cover working of any computer vision model that may be suitable for the detection of one or more features of the biological sample, without deviating from the scope of the present disclosure. Further, the data processing engine 210 may include a feature detector module (not shown), a feature matching module (not shown), a homography matrix calculator (not shown), a canvas generator (not shown), a stitching module (not shown), and a blending module (not shown). The feature detector module may be configured to detect one or more features of the biological sample in the one or more tiles of the one or more images and / or videos. Examples of the feature detector module may include, but are not limited to, SIFT, ORB, SuperPoint, Optical Flow based Method like Flownet or EiteFlowNet, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of feature detection module including known, related art, and / or later developed feature detection module. In some aspects, the one or more features may be selected from a group including, but not limited to, cell counts, cell types, cell morphology, or presence of abnormal cells or structures specific to the biological sample type or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of features including known, related art, and / or later developed features, without deviating from the scope of the present disclosure. Based on the detected one or more features, the feature matching module may be configured to compare the one or more detected features of the biological sample with the pre-trained data of the biological sample stored in the database. Examples of the feature matching module may include, but are not limited to, Brute Force Matcher, FEANN Based Matcher, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of feature matching module including known, related art, and / or later developed feature detection module. Further, the homography matrix calculator may be configured to calculate the homography matrix of the biological sample based on the one or more matched features or Optical Flow of the optical flow model of the detected feature. Further, the canvas generator module is configured to generate a canvas by way of the homography matrix calculated by the homography matrix calculator. Further, the stitching module is configured to stitch the frames generated by the canvas generator and further blending modules are configured to blend the stitched whole slide image of the biological sample. The detector module 212 may be configured to detect one or more features in the whole slide image received from data processing engine 210. In some aspects, the one or more features may be selected from a group including, but not limited to, cell counts, cell types, cell morphology, or presence of abnormal cells or structures specific to the biological sample type or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of features including known, related art, and / or later developed features, without deviating from the scope of the present disclosure. In some aspects, the detector module 212 may be selected from a group including , but not limited to, a SIFT, ORB, SuperPoint or Optical Flow based Method like FlowNet or LiteFlowNet or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the model including known, related art, and / or later developed model, without deviating from the scope of the present disclosure.

[0079] The first classifier 214 may be adapted to classify one or more detected features based on morphological data stored in the database. In some aspects, the first classifier 214 may be adapted to classify the detected one or more features into red blood cells, white blood cells and platelets based on the morphological data stored in the database. In some aspects, the morphological data may be selected from a group, including, but not limited to, an angled cells, borderline ovalocytes, burr cells, fragmented RBCs, ovalocytes, tear drops, sickle cells, band cells, basophil, blast cells, eosinophil, erythrocytes, ig, lymphocytes, metamyelocytes, monoblast, monocyte, myeloblast, myelocyte, neutrophil, promyelocyte, normal platelets, giant platelets, hypo granular platelets, micro platelets and agranular platelets or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the morphological data including known, related art, and / or later developed morphological data, without deviating from the scope of the present disclosure. In some aspects, the first classifier 214 may classify the detected one or more features into red blood cells based on the morphological data selected from a group including, but not limited to, an angled cells, borderline ovalocytes, burr cells, fragmented RBCs, ovalocytes, tear drops, and sickle cells or the like. In some aspects, the first classifier 214 may classify the detected one or more features into white blood cells based on the morphological data selected from a group including, but not limited to, band cells, basophil, blast cells, eosinophil, erythrocytes, ig, lymphocytes, metamyelocytes, monoblast, monocyte, myeloblast, myelocyte, neutrophil and promyelocyte or the like. In some aspects, the first classifier 214 may classify the detected one or more features into platelets based on the morphological data selected from a group including, but not limited to, normal platelets, giant platelets, hypo granular platelets, micro platelets and agranular platelets or the like. In some aspects, the first classifier 214 may be selected from a group, but not limited to, a You Only Look Once (YOLO), RCNN, AlexNet or Grounding Foundation Model such SAM, Florence 2b, Grounding Dino, Grounding SAM or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the classifier including known, related art, and / or later developed classifier, without deviating from the scope of the present disclosure.

[0080] The second classifier 216 may be adapted to classify the one or more detected features based on morphological defects. In some aspects, the second classifier 216 may be adapted to classify the detected red blood cells and white blood cells based on morphological defects. In some aspects, the second classifier 216 may be adapted to further classify the classified (by the first classifier) red blood cells and white blood cells based on morphological defects. In some aspects, the morphological defects may be selected from a group including, but not limited to, anisocytosis, poikilocytosis, hypochromia, hyperchromia, polychromasia, Howell- Jolly bodies, basophilic stippling, Pappenheimer bodies, Heinz bodies, nucleated red blood cells, hypersegmented neutrophils, hyposegmented neutrophils, toxic granulation, Dohle bodies, vacuolation, Auer rods, atypical lymphocytes, blast cells or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the morphological defects including known, related art, and / or later developed morphological defects, without deviating from the scope of the present disclosure. In some aspects, the second classifier 216 may be a computer vision model, selected from a group including, but not limited to, a Convolutional Neural Network (CNN) and Multi Modal Large Language Model or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the classifier including known, related art, and / or later developed classifier, without deviating from the scope of the present disclosure.

[0081] The report generation engine 218 may be communicatively coupled with the first classifier 214 and second classifier 216 respectively. The report generation engine 218 may be configured to generate reports based on one or more features classified by the first classifier and one or more features classified by the second classifier 216. The report generation engine 218 may be configured to generate reports based on one or more features classified by the first classifier 214 based on the morphological data and one or more features classified by the second classifier 216 based on the morphological defects. The generated report may include detailed information about the detected one or more features, classified features, and quantitative measurements, as well as potential diagnostic implications.

[0082] The display engine 220 may facilitate the display of generated the report to the user device 106 for display to a user. The user may include a pathologist or other medical professional. The display engine 220 may facilitate the display of generated the report to the user device 106 for display to the pathologist or other medical professional. In operation, the user device 106 may receive a report generated by the information processing apparatus through the third communication interface. The report may be displayed on the user interface 122, allowing the pathologist to review the detected objects, classified features, and quantitative measurements from the biological sample analysis. The pathologist may use the user interface 122 to examine specific areas of interest in more detail or to add annotations to the analysis results. If additional analysis or information is needed, the pathologist may use the user interface 122 to send requests back to the information processing apparatus through the third communication interface. This may trigger further processing or retrieval of additional data, with the results being sent back to the user device 106 for display. The device processing unit may manage these interactions and update the display accordingly. In some cases, this interface may also support bidirectional communication, allowing for user input or requests for additional analysis. The user interface 122 may provide a graphical interface for pathologists or other medical professionals to view and interact with the analysis results. This interface may display the generated reports, including visualizations of detected objects, classified features, and quantitative measurements. In some aspects, the generated report may be displayed on a Lab Management software.

[0083] In some aspects, the processing circuitry may perform quantitative analysis of analytes in the biological sample based on the classified features. This analysis may involve counting cells, measuring sizes or shapes of cellular structures, or determining concentrations of specific substances within the sample. In some cases, the quantitative analysis may provide important diagnostic information about the biological sample.

[0084] FIG.3 illustrates a flow chart of a method 400 for analysis of the biological sample in accordance with an exemplary aspect of the present disclosure.

[0085] At step 402, one or more images of the biological sample may be captured by way of the imaging unit 108 of the analyzer 102. Specifically, the biological sample may be placed under the imaging unit 106 of the analyzer 102 on a slide to capture one or more real-time images and / or video of the biological sample for analysis.

[0086] At step 404, one or more captured images and / or video of the biological sample may be transmitted to the information processing apparatus 104 by way of the first communication interface 112 of the analyzer 102.

[0087] At step 406, captured one or more images and / or video of the biological sample may be received by the processing circuitry 114 of the information processing apparatus 104.

[0088] At step 408, the whole slide image may be generated based on the received one or more images and / or video of the biological sample by way of the processing circuitry 114 of the information processing apparatus 104.

[0089] At step 410, the whole slide images of the biological sample may be divided into one or more tiles by way of the processing circuitry 114 of the information processing apparatus 104. At step 412, one or more features of the biological sample may be detected in the one or more tiles of the one or more images and / or videos of the biological sample by way of the processing circuitry 114 of the information processing apparatus 104. At step 414, the homology matrix may be calculated based on the detected one or more features by way of the processing circuitry 114 of the information processing apparatus 104.

[0090] At step 416, the one or more images may be stiched together to generate the whole slide image by way of the processing circuitry 114 of the information processing apparatus 104.

[0091] At step 418, one or more features may be detected in the generated whole slide image by way of the processing circuitry 114 of the information processing apparatus 104.

[0092] At step 420, one or more detected features may be classified based on morphological data stored in the database, by way of the processing circuitry 114 of the information processing apparatus 104.

[0093] At step 422, one or more detected features may be classified based on morphological defects stored in the database, by way of the processing circuitry 114 of the information processing apparatus 104.

[0094] At step 424, the report may be generated for a user by way of the processing circuitry 114 of the information processing apparatus 104.

[0095] At step 426, the generated report may be displayed to the user, by way of the user interface 122 of the user device.

[0096] At step 428, the user may provide inputs, by way of the user interface 122 of the user device.

[0097] At step 430, the report may be generated based on the report and the input received from the user.

[0098] In some aspects of the present disclosure, method 400 for analysis of the biological sample may segment the captured whole slide image by way of computer vision model for the estimation of the features in the biological sample. The image segmentation of the biological sample may be performed by the image segmentation model. The Image Segmentation model may include but is not limited to U-Net, SegNet, DeepLab, PSPNet (Pyramid Scene Parsing Network), Mask R- CNN, Segment Anything (SAM) or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the model including known, related art, and / or later developed model, without deviating from the scope of the present disclosure.

[0099] In some aspects of the present disclosure, method 400 for analysis of the biological sample may advance classify the captured whole slide image of the biological sample by way of the vision foundation model. In another embodiment of the present disclosure, the advanced classification of the biological sample may be performed by way of a multi modal large language model.

[0100] In some aspects of the present disclosure, the user may load the urine sample in a microscope. The whole slide image or video feed is taken from the microscope and analysis is done in real-time on the live feed, where all the above features may be detected using the Object Detection Model for every frame. All the parameters may be detected using an Object Detection Model. As the user clicks the detailed analysis, the cumulated report for the whole sample is generated with all the above features.

[0101] In some aspects of the present disclosure, the user may load a semen sample under the microscope, the live feed is taken from the microscope and analysis is given in real-time on the feed, where all the above features are detected in real-time using Object Detection, and Classification model. For counting, the Object Detection Model detects the sperm heads, and then each sperm is given an ID using a Kalman Filter and CNN-based Tracker. Further, each unique ID sperm head is attributed to the count. Further, using the IDs of the sperm head, the tracks for the movement of each sperm head are obtained which is then used to compute the mortality of each sperm. The Object Detection model along with a Classifier based on CNN is used to detect and then classify each individual sperm into various defects. As the user clicks the detailed analysis, a cumulated report for the sample is generated with all the above features.

[0102] In some aspects of the present disclosure, the user may put a blood sample on a slide under the microscope. The user records the amount of blood used to stain the slide. The volume of the blood sample may be used to calculate the different disease features in association with the Object Detection Model. The Segmentation Algorithm is used to determine the area, width, and morphology of the blood cells. As the user selects the workflow, the live feed is opened which projects the whole slide or video feed from the microscope to the display of the user device. The Object Detection Model along with Segmentation Algorithms and image processing is used to detect and classify different classes of blood cells. As the user selects the detailed report, all the findings for the biological sample may be summarized and reported for all the features in real time. Examples of the detected objects of the blood cell may include, but are not limited to, red blood cells (RBCs), white blood cells (WBCs), platelets, and the like. Furthermore, examples of the different red blood cells classification morphologies may include, but are not limited to angled cells, borderline ovalocytes, burr cells, fragmented RBCs, ovalocytes, tear drops, and sickle cells. Furthermore, examples of the different white blood cells classification morphologies may include, but are not limited to band cells, basophils, blast cells, eosinophils, erythrocytes, Ig, lymphocytes, metamyelocytes, monoblasts, monocytes, myeloblasts, myelocytes, neutrophils, and promyelocytes. Furthermore, examples of the different platelet classification morphologies may include, but are not limited to normal platelets, giant platelets, hypo-granular platelets, micro platelets, and agranular platelets.

[0103] In some aspects of the present disclosure, the user may load a stool sample in the microscope, the whole slide image or video feed is taken from the microscope and analysis is done in real-time on the live feed, where all the above features are detected using the Object Detection Model for every frame. All the disease features are detected using the Object Detection Model. As the user clicks the detailed analysis, the cumulated report for the whole biological sample is generated with all the above features. The foregoing discussion of the present disclosure has been presented for purposes of illustration and description. It is not intended to limit the present disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the present disclosure are grouped together in one or more aspects, configurations, or aspects for the purpose of streamlining the disclosure. The features of the aspects, configurations, or aspects may be combined in alternate aspects, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention. The present disclosure requires more features than are expressly recited in each aspect. Rather, as the following aspects reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, configuration, or aspect. Thus, the following aspects are hereby incorporated into this Detailed Description, with each aspect standing on its own as a separate aspect of the present disclosure.

[0104] Moreover, though the description of the present disclosure has included a description of one or more aspects, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the present disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, configurations, or aspects to the extent permitted, including alternate, interchangeable and / or equivalent structures, functions, ranges or steps to those disclosed, whether or not such alternate, interchangeable and / or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

[0105] As one skilled in the art will appreciate, the system 100 includes a number of functional blocks in the form of a number of units and / or engines. The functionality of each unit and / or engine goes beyond merely finding one or more computer algorithms to conduct one or more procedures and / or methods in the form of a predefined sequential manner, rather each engine explores adding up and / or obtaining one or more objectives contributing to an overall functionality of the system 100. Each unit and / or engine may not be limited to an algorithmic and / or coded form but rather may be implemented by way of one or more hardware elements operating together to achieve one or more objectives contributing to the overall functionality of the system 100. Further, as will be readily apparent to those skilled in the art, all the steps, methods and / or procedures of System 100 are generic and procedural in nature and are not specific and sequential.

[0106] Certain terms are used throughout the following description and aspects to refer to particular features or components. As one skilled in the art will appreciate, different people may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name but not structure or function. While various aspects of the present disclosure have been illustrated and described, it will be clear that the present disclosure is not limited to these aspects only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the present disclosure.

[0107] Advantages of the Present Invention

[0108] Thus, the system 100 may provide following advantages that may be derived from the structural and functional aspects of the system 100: -

[0109] The system reduces turnaround time and increases throughput, enabling faster sample processing and quicker diagnostic results.

[0110] The remote viewing allows pathologists to access the sample and AI- generated report from anywhere, enhancing flexibility and collaboration.

[0111] The system analyses the biological sample and ensures standardized, consistent reports, eliminating inter-lab variability and improving diagnostic accuracy.

[0112] The final, standardized report is shared with the Lab Management Software, ensuring seamless integration into the broader lab workflow and helping maintain a smooth chain of communication from analysis to interpretation.

[0113] Aspects of the present disclosure are discussed here with reference to flowchart illustrations and block diagrams that depict methods, systems, and apparatus in accordance with various aspects of the present disclosure. Each block within these flowcharts and diagrams, as well as combinations of these blocks, can be executed by computer-readable program instructions. The various logical blocks, modules, circuits, and algorithm steps described in connection with the disclosed aspects may be implemented through electronic hardware, software, or a combination of both. To emphasize the interchangeability of hardware and software, the various components, blocks, modules, circuits, and steps are described generally in terms of their functionality. The decision to implement such functionality in hardware or software is dependent on the specific application and design constraints imposed on the overall system. Person having ordinary skill in the art can implement the described functionality in different ways depending on the particular application, without deviating from the scope of the present disclosure.

Claims

We Claim:

1. An information processing apparatus (104), comprising: processing circuitry (114), coupled to an analyzer (102), configured to: a. receive one or more images of a biological sample; b. generate a whole slide image of the biological sample from the one or more images, such that the generation of the whole slide image comprises: i. detect one or more features in one or more images; ii. calculate a homography matrix based on the one or more detected features; and iii. stitch the one or more images together based on the homography matrix to generate the whole slide image; c. detect, by way of a detector module (212), one or more features in the whole slide image; d. classify, by way of a first classifier (214), one or more detected features based on morphological data, e. classify, by way of a second classifier (216), one or more detected features based on morphological defects, and f. generate a report comprising i) one or more features classified by the first classifier (214); and ii) one or more features classified by the second classifier (216).

2. The information processing apparatus (104) as claimed in claim 1, wherein the biological sample is selected from a group comprising blood, sweat, urine, blood serum, semen, breast milk, saliva, blood plasma, tears, mucus, cerebrospinal fluids, amniotic fluid, vaginal lubrication fluids, pus, lymph, bile, synovial fluid, aqueous humour, phlegm, gastric acid, pre-ejaculate, or colostrum.

3. The information processing apparatus (104) as claimed in claim 1, wherein the one or more features are selected from a group comprising at least one of, cellcounts, cell types, complex cells, cell morphology, or presence of abnormal structures or structure specific to biological sample.

4. The information processing apparatus (104) as claimed in claim 1, wherein the detector module (212) is selected from a group comprising, but not limited to, a SIFT, ORB, SuperPoint or Optical Flow based Method like FlowNet or LiteFlowNet.

5. The information processing apparatus (104) as claimed in claim 1, wherein the first classifier (214) is selected from a group comprising, but not limited to, a YOLO, RCNN, AlexNet or Grounding Foundation Model such SAM, Florence 2b, Grounding Dino, and Grounding SAM.

6. The information processing apparatus (104) as claimed in claim 1, wherein the second classifier (216) comprises a Multi Modal Large Language Model.

7. The information processing apparatus (104) as claimed in claim 1, wherein the morphological defects are selected from a group comprising anisocytosis, poikilocytosis, hypochromia, hyperchromia, polychromasia, Howell-Jolly bodies, basophilic stippling, Pappenheimer bodies, Heinz bodies, nucleated red blood cells, hypersegmented neutrophils, hyposegmented neutrophils, toxic granulation, Dbhle bodies, vacuolation, Auer rods, atypical lymphocytes orblast cells.

8. The information processing apparatus (104) as claimed in claim 1, wherein the processing circuitry (114) is further configured to: a. display the generated report to a user; b. receive an input from the user; and c. generate the report based on the displayed report and the input received from the user.

9. A system (100) for analyzing biological samples, comprising:an analyzer (102) configured to capture one or more images of the biological sample; an information processing apparatus (104) comprising a processing circuitry (114) that is communicatively coupled with the analyzer (102), and configured to: a. receive one or more images of a biological sample; b. generate a whole slide image of the biological sample from the one or more images, such that the generation of the whole slide image comprises: i. detect one or more features in one or more images; ii. calculate a homography matrix based on the one or more detected features; and iii. stitch the one or more images together based on the homography matrix to generate the whole slide image; c. detect, by way of a detector module (212), one or more features in the whole slide image; d. classify, by way of a first classifier (214), one or more detected features based on morphological data, e. classify, by way of a second classifier (216), one or more detected features based on morphological defects, and f. generate a report comprising i) one or more features classified by the first classifier (214); and ii) one or more features classified by the second classifier (216).

10. The system (100) as claimed in claim 9, wherein the biological sample is selected from a group comprising blood, sweat, urine, blood serum, semen, breast milk, saliva, blood plasma, tears, mucus, cerebrospinal fluids, amniotic fluid, vaginal lubrication fluids, pus, lymph, bile, synovial fluid, aqueous humour, phlegm, gastric acid, pre-ejaculate, or colostrum.

11. The system (100) as claimed in claim 9, wherein the one or more features are selected from a group comprising at least one of, cell counts, cell types, complexcells, cell morphology, or presence of abnormal structures or structure specific to biological sample.

12. The system (100) as claimed in claim 9, wherein the detector module (212) is selected from a group comprising, but not limited to, a SIFT, ORB, SuperPoint or Optical Flow based Method like FlowNet or LiteFlowNet.

13. The system (100) as claimed in claim 9, wherein the first classifier (214) is selected from a group comprising, but not limited to, a YOLO, RCNN, AlexNet or Grounding Foundation Model such SAM, Florence 2b, Grounding Dino, and Grounding SAM.

14. The system (100) as claimed in claim 9, wherein the second classifier (216) comprises a Multi Modal Large Language Model.

15. The system (100) as claimed in claim 9, wherein the morphological defects are selected from a group comprising anisocytosis, poikilocytosis, hypochromia, hyperchromia, polychromasia, Howell-Jolly bodies, basophilic stippling, Pappenheimer bodies, Heinz bodies, nucleated red blood cells, hypersegmented neutrophils, hyposegmented neutrophils, toxic granulation, Dbhle bodies, vacuolation, Auer rods, atypical lymphocytes or blast cells.

16. The system (100) as claimed in claim 9, wherein the processing circuitry (114) is further configured to: a. display the generated report to the user. b. receive input from the user; and c. generate the report based on the displayed report and the input received from the user.

17. A method (400) for analyzing biological samples, comprising: a. receiving (402), by way of processing circuitry (114), one or more images of a biological sample;b. generating (408), by way of processing circuitry (114), a whole slide image of the biological sample from the one or more images, wherein generating the whole slide image comprises: i. detecting (412)one or more features in one or more images; ii. calculating (414) a homography matrix based on the one or more detected features; and iii. stitching (416) the one or more images together based on the homography matrix to generate the whole slide image; c. detecting (418), by way of a detector module (212), one or more features in the whole slide image; d. classifying (420), by way of a first classifier (214), one or more detected features based on morphological data, e. classifying (422), by way of a second classifier (216), one or more detected features based on morphological defects, and f. generating (424) a report comprising i) one or more features classified by the first classifier (214); and ii) one or more features classified by the second classifier (216).

18. The method (400) as claimed in claim 17, wherein the processing circuitry (114) is further configured to: a. displaying (426) the generated report to the user; b. receiving (428) input from the user; and c. generating (430) the report based on the displayed report and the input received from the user.

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