Diagnostic imaging system

Abstract

A diagnostic imaging system for analysing skin tissue image data received from a remote user for evaluating the image data for screening for a skin disease or skin condition, the system comprising: a manipulator arm and slider system of a robot, the manipulator arm moving on the slider along a patient body and comprising a plurality of elements interconnected to each other by a plurality of joints whereby each element is rotatable relative to an adjoining element of the manipulator arm; a non-invasive dermatoscope module comprising one or more dermatoscopes arranged to acquire tissue image data, the dermatoscope module being coupled to one of the elements of the manipulator arm by a coupling arrangement to allow movement of the dermatoscope module relative to said one of the elements of the manipulator arm; and a controller coupled to the manipulator arm and a coupling arrangement to employ the manipulator arm to move the dermatoscope module relative to the subject's anatomy; a server in communication with a distribution network for receiving tissue image data from the dermatoscope module, the tissue image data; a processor for executing a learning machine algorithm, wherein the algorithm is trained using image data sets having known outcomes for a plurality of skin diseases or skin conditions, the processor being operable for receiving image data from the dermatoscope module and processing the image data to produce processed image data and introducing the processed image data into the learning machine to provide an output indication of whether the tissue image data is associated with a skin condition or skin disease.

Description

Diagnostic Imaging System TECHNICAL FIELD

[0001] The present invention relates to a local or remote skin diagnostic imaging system (SDIS) which is particularly suitable for detecting conditions such as melanomas and other skin disorders. BACKGROUND

[0002] Any references to methods, apparatus or documents of the prior art are not to be taken as constituting any evidence or admission that they formed, or form part of the common general knowledge.

[0003] The current landscape of dermatological care faces challenges, particularly in injury to healthcare workers, the accessibility of remote communities and the accuracy and diagnosis of skin checks. Issues such as limited access to dermatologists in rural areas, inconsistent diagnostic results, early detection difficulties, and technological limitations hinder effective skin screening and management for conditions such as cancer.

[0004] In recent years, the importance of early detection in combating skin cancer and other dermatological conditions has become increasingly apparent. Australia has one of the highest rates of melanoma in the world, often referred to as the country's national cancer. It is the third most common cancer in men and the fourth most common in women, with an estimated 16,800 new cases and approximately 1,300 deaths annually. Melanoma is particularly prevalent among Australians aged 20 to 39,making it the most common cancer for this age group. Traditional skin check procedures, while effective, are often hindered by accessibility issues, inconsistent diagnostic accuracy, patient compliance challenges, as well as ergonomic issues for specialist medical professionals.

[0005] It would be desirable to address some of the above-mentioned problems and provide an improved diagnostic system for diagnosing melanoma and other skin conditions. SUMMARY OF INVENTION

[0006] In one aspect, the invention provides a diagnostic imaging system for remotely analysing skin tissue image data received from a patientfor evaluating the image data for screening for a skin disease or skin condition the system comprising: a manipulator arm of a robot, the manipulator arm comprising a plurality of elements interconnected to each other by a plurality of joints whereby each element is rotatable relative to an adjoining element of the manipulator arm wherein at least one of the elements is mounted on a fixed or mobile base; a non-invasive skin analysis module (SAM) comprising one or more dermatoscopes arranged to acquire tissue image data, the dermatoscope module being coupled to one of the elements of the manipulator arm by a coupling arrangement to allow movement of the dermatoscope module relative to said one of the elements of the manipulator arm; and a controller coupled to the manipulator arm and a coupling arrangement to employ the manipulator arm to move the dermatoscope module relative to the subject’s anatomy;a server in communication with a distribution network for receiving tissue image data from the dermatoscope module, the tissue image data; a processor for processing image data to provide an output indication of whether the tissue image data is associated with a skin condition or skin disease.

[0007] In an embodiment, the processor is arranged for executing a learning machine, wherein the learning machine is trained using image data sets having known outcomes for a plurality of skin diseases or skin conditions, the processor being operable for receiving image data from the dermatoscope module and processing the image data to produce processed image data and introducing the processed image data into the learning machine for providing said output indication.

[0008] In another aspect, the invention provides a diagnostic imaging method for analysing skin tissue image data received from a patient for evaluating the image data for screening for a skin disease or skin condition, the method comprising: positioning a manipulator arm of a robot in close proximity to the patient, the manipulator arm comprising a plurality of elements interconnected to each other by a plurality of joints whereby each element is rotatable relative to an adjoining element of the manipulator arm wherein at least one of said elements is mounted on a fixed or mobile base; wherein a non-invasive dermatoscope module comprising one or more dermatoscopes arranged to acquire tissue image data, is coupled to one of the elements of the manipulator arm by a coupling arrangement to allow movement of the dermatoscope module relative to said one of the elements of the manipulator arm; andcontrolling movement of the manipulator via a controller coupled to the manipulator arm and a coupling arrangement to employ the manipulator arm to move the dermatoscope module relative to the patient’s anatomy; acquiring tissue image date from the dermatoscope module and transmitting the tissue image data to a remote server in communication with a distribution network; processing the tissue image data in a processor, and utilizing the processed image data for providing an output indication of whether the tissue image data is associated with a skin condition or skin disease.

[0009] In an embodiment, the processor is programmed for executing a learning machine, the learning machine being trained using image data sets having known outcomes for a plurality of skin diseases or skin conditions, to produce processed image data and introducing the processed image data into the learning machine.

[0010] In an embodiment, the controller and the coupling arrangement are arranged to move the dermatoscope module to one of a plurality of image acquisition zones of the subject’s anatomy.

[0011] In an embodiment, movement of the dermatoscope module is based on a selection of one out of a plurality of operating modes wherein each operating mode corresponds to a respective image acquisition zone such that selection of an operating mode positions the elements of the manipulator arm into a predetermined starting positioncorresponding to the selected mode before allowing further movement of the one or more of the joints of the manipulator arm in the selected mode, by using the controller, within a pre- defined range for the selected mode to limit movement of the skin analysis module within the image acquisition zone of the subject’s anatomy that corresponds to the selected operating mode.

[0012] In an embodiment, the controller comprises a control interface to allow selection of one of the plurality of operating modes by the patient and / or an operator.

[0013] In an embodiment, the manipulator arm comprises at least six degrees of freedom for imparting movement to the ultrasound probe module.

[0014] In an embodiment, the processor is arranged to process the image data to extract features from the image data which forms at least a part of the processed image data and wherein introducing the extracted features into the learning machine produces an output comprising a recognized pattern thereby providing an indication of whether the tissue image data is associated with a skin condition or skin disease.

[0015] In an embodiment, the server is operable to communicate the output indication to the remote operator.

[0016] In an embodiment, the diagnostic imaging system further comprises: a computing module including a processor arranged to generate a virtual model of the manipulator arm and the non-invasive skin analysis module based on one or more signals received from one or more sensors positioned relative to the manipulator arm and the skin analysis module and based on control input received from one or more users; and a simulation module for generating real-time simulation based on the virtual model.

[0017] In an embodiment, the controller is arranged to process information from the real-time simulation and generating control commands for controlling movement of the manipulator arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Preferred features, embodiments and variations of the invention may be discerned from the following Detailed Description, which provides sufficient information for those skilled in the art to perform the invention. The Detailed Description is not to be regarded as limiting the scope of the preceding Summary of the Invention in any way. The Detailed Description will make reference to a number of drawings as follows: Figure 1 is an isolated perspective of a primary manipulator arm 100 that forms a part of a diagnostic imaging system 1000 in accordance with an embodiment. Figure 2 depicts the primary manipulator arm 100 in the broader network context which also forms part of the diagnostic imaging system 1000. FIG. 3 is a block diagram of the basic architecture for a system according to the invention. Figure 4A is a box diagram depicting various components of the diagnostic imaging system. Figurre 4B is a box diagram depicting the optional use of a virtual twin within the diagnostic imaging system. Figure 5 is an in-use view of the diagnostic imaging system 1000. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0019] In a broad aspect, the invention provides a mobile, portable or fixed skin condition imaging system 1000 which includes one or more robotic manipulator arms (as will be discussed in detail in the foregoing sections) for facilitating remote control of a robotic dermatological imaging system. Whilst, the present examples are limited to melanoma screening, the scope of the invention is in no way limited to melanoma screening only and could therefore be applied for imaging the skin of the user for diagnosing other conditions by a range of healthcare people such as dermatologist, GP’s, specialist, surgeons ect.

[0020] Referring to Figures 1 and 2 in particular, the diagnostic imaging system 1000 comprises a manipulator arm 100 such as a collaborative robot. In the presently described embodiment, the manipulator arm 100 comprises a plurality of movable elements 5, 6, 7, 8, 9, 10, 11 and 12 that are interconnected to each other by joints 4A to 4Gto effect rotational movement between adjacent elements. Element 5 may be positioned on a base (as shown in Figure 2) which could either be fixed or be movably positioned on a trolley 140.

[0021] The robotic manipulator 100 comprises a controllable or sliding base with several movable parts that can move along a plurality of axes, each of the axes being located at the joints 4A to 4F with the each of the elements 5 to 12 with a skin analysis module 200 having one or more cameras and / or one or more dermatoscopes being attached to lever 11. For the purposes of simplicity, each of the axes at joints 4A to 4F will be depicted by axes 1-8 in the written description. Each of the axes 1- 8 is moved with a drive, for example an electric drive, which are electrically connected in a non- depicted manner to a controller 300 which includes a computer 310, so that thecontroller computer 310 or a computer program running on control computer 310 is able to activate electric drives in the joints 4A to 4G in such a way that the position of element 12 to which the skin analysis module 200 can be oriented essentially freely in space (6 DOF motion). The electric drives of the manipulator arm 100 each include for example an electric motor and possibly power electronics that activate the motors.

[0022] Movement between the plurality of elements along joints 4A to 4F is effected about each of the axes 1-6 with a drive, preferably electric drives so that the controller 300 is able to activate the electric drives in such a way so as to position or orient the skin analysis module 200 or its centre point freely in space.

[0023] In the case of the present exemplary embodiment, control computer 310 is programmed or designed in such a way that it, or a computer program running on it, is able to limit movement within a working region of the patient’s body. For example, the operator may choose to acquire images of the patient’s skin on their forehead or head region in one operable working mode. In another operable working mode, the operator may choose to acquire images of the patient’s skin on their back region. Operation in each mode requires limiting movement of the primary manipulator 100 and the limiting of movement may correspond to different operable modes. It is important to note that for each operable mode, the SAM 200 may be positioned uniquely to carry out imaging of skin conditions. The unique starting position coordinates for each operable mode may be saved on a memory device that is in communication or a part of the controller computer 310. These starting points may be adjusted using 3D imaging of the patient body to position the SAM in unique positions.

[0024] During use, a subject or patient may lay stand, down or be seated, and the manipulator robot 100 may be manually or autonomously guided to a location close to the subject. This location may be a fixed reference location. The initial placement location for the primary manipulator robot 100 and SAM 200 may vary depending on the physical characteristics of the subject. A dynamic placement location for the primary manipulator robot 100 may be computed based upon one or more markers positioned on the patient’s anatomy.

[0025] The SAM 200 may utilise images (including

[0022] , which uses mobile phones, in- room and onboard cameras for dermoscopic image analysis). Dermoscopic images are taken with the aid of liquid medium or nonpolarised light source and magnifiers, under well-controlled clinical conditions. Dermoscopic images include features below the skin surface. Generally, an automatic melanoma detection system can be divided into three main stages of segmentation, feature extraction, and classification. Some algorithms have been investigated for dermoscopic images taken under well- controlled conditions. The movement of the dermatoscope module may be controlled to be focussed on a specific working region of the subject’s body by selection of one of a number of selection modes.

[0026] Figure 3 provides a high-level box diagram showing the modular components of the screening process using the manipulator robot 100. The person wishing to obtain a skin screening is positioned at a screening location that is in close proximity to the manipulator robot 100. Thereafter, the movement of the dermatoscope module 200 is controlled to position the SAM 200 to acquire images of a specific working region of the subject’s body.

[0027] In an initial step 502, the person wishing to obtain a melanoma screening provides requested information by accessing an input interface such as a display device associated with the diagnostic imaging system 1000. An input registration step may be carried out by asking a series of questions to the person. In addition to identification or registration information, in the initial step 504, the requested information may include a series of questions that will better enable the image analysis and classification process to evaluate the requester's risk of melanoma or related skin condition. Moreover, the information provided by the person wishing to obtain the melanoma screening may be utilised for communication with the controller 300 to position the manipulator arm 100 such that the skin analysis module 200 is appropriately oriented to carry out image acquisition for melanoma screening. In other embodiments, further algorithms may be developed for detecting other skin conditions. The questions may include dimensional information, how long ago the suspect lesion was noticed, any changes, etc. The person may use a iPad, computer or smart phone for providing user input.

[0028] An output interface may also be provided to give the person (being screened) suitable directions to be positioned at a suitable screening location before deploying movement of the manipulator arm 100 by sending control signals and control input to the controller 300.

[0029] Once the person being screened has been positioned at the suitable screening location, the dermatoscope module 200 may be deployed to acquire a plurality of images to assess the possibility of melanoma and carry out the desired screening process. The images and any image data may be transmitted from the collaborative robot system to a remote server or local server by any conventional data transmissionmethods. The remote or local server functions as an image analysis server that is programmed to execute pre-processing algorithms for extracting relevant features and a classifier that has been trained to distinguish between melanoma and other conditions is used to process the input image (step 508) to assign the suspected cancer to one of a small number of risk categories ranging from low to high, or to provide a probability which is considered a “risk score”. In an alternative method step, image data may be processed without the algorithms to provide visual images to the operator to allow the operator to make an initial visual assessment to determine if the skin associated with the image data. In step 510, a report in the form of the risk score may be transmitted to the person being screened. The image analysis server may comprise a “learning machine”. Learning machines comprise algorithms that may be trained to generalise using data with known outcomes. Trained learning machine algorithms may be applied to predict the outcome in cases of unknown outcome. Artificial Intelligence (AI) algorithms, models and techniques such as Supervised / Unsupervised / Reinforcement Learning, Deep and Machine Learning, Natural Language Processing, Generative AI, Evolutionary, Fuzzy Logic or probabilistic algorithms can form part of the automation and autonomous behavior of the system 1000. Machine-learning approaches, which can include algorithms such as Neural Networks, Generative Networks, hidden Markov models, belief networks and support vector machines, are ideally suited for domains characterized by the existence of large amounts of data, noisy patterns and the absence of general theories. Particular focus among such approaches has been on the application of artificial neural networks to biomedical image analysis, with results reported in the use of neural networks for analyzing images of the skin. The majority of learning machines that have been applied to image analysis are neural networks trained using back-propagation, agradient-based method in which errors in classification of training data are propagated backwards through the network to adjust the bias weights of the network elements until the mean squared error is minimized. Additionally, large language models can be used to augment these approaches to enhance the classification of skin conditions.

[0030] In a final step 512, the image analysis server may search a database to provide the requester with referrals to a physician who specializes in treatment of skin cancers. The present invention is not intended to provide a definitive diagnosis or not of melanoma; it is merely intended to serve as a simple, low-cost preliminary screening tool to allow a person to obtain an advisory indication of whether he or she should seek a formal medical evaluation.

[0031] Referring to Figure 4A, a high-level box diagram has been provided to demonstrate the working and interrelationship between various parts of the system 1000. The remote robotic skin check system 1000 is designed to integrate advanced technologies for comprehensive and precise dermatological examinations as illustrated in the figures below. The system 1000 features the robotic manipulator arm 100 that may be equipped with a force sensor, an RGB-D camera, and a dermatoscope / imaging device within the skin analysis module 200. The force sensor measures applied force, providing haptic feedback to ensure precision during examinations. The RGB-D camera captures high-resolution images and depth information, while the dermatoscope offers magnified, detailed images of the skin, allowing for thorough examination of lesions and other conditions.

[0032] In addition to these core components, a 360-room camera may also be utilized for providing a comprehensive view of the examination room, adding contextual visual information. The skin imaging system 1000 may be controlled remotely by a doctorusing a controller and a doctor control station (part of the controller module 300), which serves as the central hub for monitoring and operating the robotic arm. The control station may integrate interfaces for image viewing, data analysis, and robotic control. To enhance the remote examination experience, the system includes an XR viewer and XR headset, providing the doctor with an immersive, interactive view of the patient's skin.

[0033] The software components, including the perception software may play an important role in processing and managing the data captured by the skin analysis module devices. The perception software processes images and depth data, converting them into usable diagnostic information. A separate application may be used to manage the robotic manipulator arm 100's operations, integrating input from the perception software and control commands from the doctor. Additionally, a controller parser may be used to interpret commands from the controller 300, translating them into precise movements for the robotic manipulator arm 100.

[0034] As shown in Figures 4A and 4B, the robotic manipulator arm 100 may be slidably mounted on a track in an alternative working configuration.

[0035] In practice, the robotic manipulator arm 100, equipped with its sensors and cameras, is set up in the examination room. The doctor operates the system remotely from the control station, using the controller 300 to maneuver the arm and capture high-resolution images and depth data. This data is processed in real-time and displayed on the control station and XR viewer for detailed analysis. The XR headset provides the doctor with an immersive examination experience, allowing for manipulation of the virtual representation of the patient's skin to examine lesions fromvarious angles. The force sensor ensures the robotic arm applies the correct pressure, and the controller parser translates the doctor’s inputs into precise movements. This integrated approach significantly improves diagnostic accuracy and accessibility, particularly in remote and underserved areas.

[0036] The diagnostic imaging system 1000 is also equipped with advanced autonomous capabilities that allow it to conduct preliminary skin checks without direct human intervention. Utilizing the high-resolution camera, dermatoscope / imaging device, and AI-driven perception software, the robotic manipulator arm 100 can systematically scan the patient's skin, identify potential areas of concern, and capture detailed images and depth data. This data is then processed by the AI algorithms to detect anomalies, such as unusual moles or lesions, and assess their characteristics. The system compiles the findings into a comprehensive preliminary report, highlighting areas that require further examination. This report, complete with annotated images and initial diagnostic insights, is automatically generated and sent to the dermatologist. By the time the dermatologist reviews the patient data, they have a detailed, AI- assisted overview of the patient's skin health, allowing them to focus on areas flagged by the system and make more informed decisions about further diagnostic procedures and treatments. This autonomous preliminary screening not only enhances efficiency but also ensures that potential issues are promptly identified and addressed.

[0037] Figure 4B illustrates the optional use of a virtual twin. Specifically, a computing module is provided that includes a processor arranged to generate a virtual model of the manipulator arm and the non-invasive skin analysis module 200 based on signals received from sensors positioned relative to the manipulator arm and the skin analysis module 200. This virtual model may be generated and based on control input received from the user trying to position the manipulator arm 100 into an image acquisition zone. Before effecting actualmovement of the manipulator arm 100 to a desired position, the actions of the user (such as selection of an operational mode by selecting an image acquisition zone), the generation of the virtual model followed by generation of a simulation module for generating real-time simulation based on the virtual model can ensure safety for subjects. Any inadvertent impact or contact with the subject may be avoided. The twin model can check intended motion of the manipulator 100 before executing actual movement to avoid unintended collisions. The use of the digital twin model also enables autonomous (and semi-autonomous) execution by allowing operators to only provide high-level commands with the digital twin providing more detailed or refined commands for movement of the manipulator 100. If there is loss in communication (during remote operation), the digital twin can maintain the manipulator 100 in a safe working state. The digital twin can also provide suuggestions and optimise path planning for planning movement of the manipulator 100. The digital twin also provides to the dermatologist a live 3D model, force visualisation, and latency compensation when video / camera view is limited.

[0038] In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. The term “comprises” and its variations, such as “comprising” and “comprised of” is used throughout in an inclusive sense and not to the exclusion of any additional features.

[0039] It is to be understood that the invention is not limited to specific features shown or described since the means herein described comprises preferred forms of putting the invention into effect.

[0040] The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims appropriately interpreted by those skilled in the art.

Claims

CLAIMS 1. A diagnostic imaging system for analysing skin tissue image data received from a patient for evaluating the image data for screening for a skin disease or skin condition, the system comprising: a manipulator arm , the manipulator arm comprising a plurality of elements interconnected to each other by a plurality of joints whereby each element is rotatable relative to an adjoining element of the manipulator arm wherein at least one of said elements is mounted on a fixed or mobile base; a non-invasive skin analysis module comprising one or more dermatoscopes arranged to acquire tissue image data, the dermatoscope module being coupled to one of the elements of the manipulator arm by a coupling arrangement to allow movement of the dermatoscope module relative to said one of the elements of the manipulator arm; and a controller coupled to the manipulator arm and a coupling arrangement to employ the manipulator arm to move the dermatoscope module relative to the subject’s anatomy; a server in communication with a distribution network for receiving tissue image data from the dermatoscope module, the tissue image data; a processor for processing image data to provide an output indication of whether the tissue image data is associated with a skin condition or skin disease.

2. A diagnostic imaging system in accordance with claim 1 wherein the processor is arranged for executing a learning machine, wherein the learning machine is trained using image data sets having known outcomes for a plurality of skin diseases or skin conditions, the processor being operable for receiving image data from the dermatoscope module and processing the image data to produce processed imagedata and introducing the processed image data into the learning machine to provide the output indication.

3. A diagnostic imaging system in accordance with claim 1 or claim 2 wherein the controller and the coupling arrangement are arranged to move the skin analysis module to one of a plurality of image acquisition zones of the subject’s anatomy.

4. A diagnostic imaging system in accordance with claim 3 wherein movement of the skin analysis module is based on a selection of one out of a plurality of operating modes wherein each operating mode corresponds to a respective image acquisition zone such that selection of an operating mode positions the elements of the manipulator arm into a predetermined starting position corresponding to the selected mode before allowing further movement of the one or more of the joints of the manipulator arm in the selected mode, by using the controller, within a pre-defined range for the selected mode to limit movement of the skin analysis module within the image acquisition zone of the subject’s anatomy that corresponds to the selected operating mode.

5. A diagnostic imaging system in accordance with claim 4 wherein the controller comprises a control interface to allow selection of one of the plurality of operating modes by the remote user and / or an operator.

6. A diagnostic imaging module in accordance with any one of the preceding claims wherein the manipulator arm comprises at least six degrees of freedom for imparting movement to the ultrasound probe module.

7. A diagnostic imaging system in accordance with any one of the preceding claims wherein the processor is arranged to process the image data to extract features from the image data, which forms at least a part of the processed image data and wherein introducing the extracted features into the learning machine produces an outputcomprising a recognized pattern, thereby providing an indication of whether the tissue image data is associated with a skin condition or skin disease.

8. A diagnostic imaging system in accordance with any one of the preceding claims wherein the server is operable to communicate the output indication to the remote user and operator.

9. A diagnostic imaging system in accordance with claim 4 further comprising: a computing module including a processor arranged to generate a virtual model of the manipulator arm and the non-invasive skin analysis module based on one or more signals received from one or more sensors positioned relative to the manipulator arm and the skin analysis module and based on control input received from one or more users; and a simulation module for generating real-time simulation based on the virtual model.

10. A diagnostic imaging system in accordance with claim 9 wherein the controller is arranged to process information from the real-time simulation and generating control commands for controlling movement of the manipulator arm.

11. A diagnostic imaging method for analysing skin tissue image data received from a remote user for evaluating the image data for screening for a skin disease or skin condition, the method comprising: positioning a manipulator arm of a robot in close proximity to the remote user, the manipulator arm comprising a plurality of elements interconnected to each other by a plurality of joints whereby each element is rotatable relative to an adjoining element of the manipulator arm wherein at least one of said elements is mounted on a fixed or mobile base; wherein a non-invasive dermatoscope module comprising one ormore dermatoscopes arranged to acquire tissue image data, is coupled to one of the elements of the manipulator arm by a coupling arrangement to allow movement of the dermatoscope module relative to said one of the elements of the manipulator arm; and controlling movement of the manipulator robot via a controller coupled to the remote or locally located manipulator robot, and a coupling arrangement to employ the manipulator robot to move the dermatoscope module relative to the remote user’s anatomy; acquiring tissue image data from the dermatoscope module and transmitting the tissue image data to a remote server in communication with a distribution network; processing the tissue image data in a processor to produce processed image data and introducing the processed image data into the learning machine; and providing an output indication of whether the tissue image data is associated with a skin condition or skin disease.

12. A diagnostic imaging method in accordance with claim 11 wherein the processor is programmed for executing a learning machine, the learning machine being trained using image data sets having known outcomes for a plurality of skin diseases or skin conditions to produce the output indication.

13. A diagnostic imaging method in accordance with any one of claims 11 or 12 further comprising the step of selecting one out of a plurality of operating modes wherein each operating mode corresponds to a respective image acquisition zone such that selection of an operating mode positions the elements of the manipulator arm into a predetermined starting position corresponding to the selected mode before allowing further movement of the one or more of the joints of the manipulator arm in the selected mode, by using the controller, within a pre-defined range for the selected mode to limitmovement of the skin analysis module within the image acquisition zone of the subject’s anatomy that corresponds to the selected operating mode.

14. A diagnostic imaging method in accordance with any one of claims 11 to 13 further comprising the step of: generating a virtual model of the manipulator arm and the non-invasive skin analysis module based on one or more signals received from one or more sensors positioned relative to the manipulator arm and the skin analysis module and based on control input received from one or more users; generating a real-time simulation based on the virtual model; and controlling the movement of the manipulator arm based on the real-time simulation.

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