DEVICE AND METHOD FOR DETECTING AND MONITORING SKIN DISEASES - Patent application

Abstract

The present invention comprises a device and method for detecting and monitoring skin diseases, the device comprising image acquisition means (11) configured to acquire medical images of at least one portion of skin affected by a skin disease, sensor means (12) configured to detect a physical condition of the portion of skin and / or an environmental condition around the portion of skin and generate disease condition data representative of the physical and / or environmental condition, memory means (13) for storing the data in digital form, and processing means (14) configured to a) read out the disease condition data detected by the sensor means (12) and the medical images acquired by the image acquisition means (11), and b) store the disease condition data and the medical images in the memory means (13).

Description

[Technical field]

[0001] The present invention relates to a device and method for detecting skin disorders, such as ulcers, erythema, etc. In particular, the present invention allows for the detection of skin disorders and the assessment of their condition, such as the degree of inflammation and / or the degree of bacterial or viral infection.

[0002] As is known, the skin is the first barrier by which the human body defends itself against the external environment.

[0003] People who are forced to spend long periods of time in the same position (e.g., people who spend a lot of time in bed or in a wheelchair, people with mobility problems, disabled people, or elderly people) or who spend time in remote areas of the earth (e.g., soldiers, people living in tribal groups, volunteers working in humanitarian agencies, etc.) often suffer from skin disorders (e.g., decubitus ulcers, rashes due to allergies or fungal infections, wounds due to insect bites or animal bites). The disorders may be due to reduced elasticity of the skin (e.g., due to excessive poor circulation or malnutrition) or living in an environment where they are forced to come into contact with animals, fungal spores, or plants that, when touched, produce a skin reaction that may worsen up to a skin disorder that requires appropriate medical / dermatological treatment.

[0004] However, these people have a hard time receiving such treatment: in fact, without a careful diagnosis by a dermatologist, treating the skin may not solve the problem but may merely alleviate its effects (e.g., using a cortisone cream for acute hives caused by an allergic reaction without knowing the allergen) or may even damage the patient's skin (e.g., using a cortisone cream to treat a fungal infection).

[0005] Thus, without a quality diagnosis by a dermatologist, these individuals suffer from chronic skin conditions that can worsen into extremely serious conditions, such as the formation of deep ulcers that may require amputation of a limb to save the patient's life.

[0006] The present invention aims to solve these and other problems by providing a device for detecting skin disorders.

[0007] It is a further object of the present invention to solve these and other problems by providing a method for detecting skin disorders.

[0008] The basic idea of ​​the present invention is to obtain, by an image acquisition means, a medical image (i.e. an image acquired by a specific procedure) of at least one portion of skin affected by a skin disease, and to generate pathology data representative of said physical and / or environmental conditions of said portion of skin (e.g. skin temperature, etc.) by means of a sensor means, by detecting said physical and / or environmental conditions around said portion of skin (e.g. ambient air temperature and / or humidity).

[0009] In this way, medical images and pathology data are made available to the dermatologist, allowing a more accurate diagnosis than would be possible if only medical images were examined, i.e. the specialist is further able to estimate the state of the skin disease. Indeed, knowledge of the physical state of the skin area and / or its surrounding environmental state allows the dermatologist to identify the disease(s) with greater accuracy and to more accurately assess the state of said disease(s), without the need to physically visit the patient.

[0010] It should be understood that skin diseases that cause skin erythema may manifest differently depending on the temperature and / or humidity of the patient's surrounding environment, or the type of skin color of the patient, etc. Thus, by using additional pathology data along with medical images, dermatologists can better understand the patient's clinical situation and identify the most suitable treatment. For example, in tropical climates, treatment with a cream containing antibiotics is more preferred to suppress bacterial proliferation promoted by high temperature and humidity values. Further advantageous features of the present invention are set out in the accompanying claims. [Brief description of the drawings]

[0011] These features, as well as further advantages of the invention, will become more apparent from the following description of one embodiment thereof, as illustrated in the accompanying drawings, given purely as a non-limiting example, in which: [Figure 1] 1 shows a block diagram of an apparatus for detecting skin disorders according to the present invention. [Diagram 2] 1 shows a flow chart of a method for detecting skin diseases according to the present invention. [Diagram 3] FIG. 1 shows a schematic diagram of a neural network implemented in at least one embodiment of the present invention. [Figure 4] FIG. 1 shows a schematic diagram of a neural network implemented in at least one embodiment of the present invention. [Diagram 5] FIG. 1 shows a schematic diagram of a neural network implemented in at least one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Any reference to "an embodiment" in this specification will indicate that a particular configuration, structure, or feature is included in at least one embodiment of the present invention. Thus, phrases such as "in an embodiment" and other similar phrases that may appear in various parts of this specification will not necessarily all relate to the same embodiment. Furthermore, any particular configuration, structure, or feature may be combined in any manner deemed appropriate in one or more embodiments. Thus, the following references are used for brevity only and do not limit the scope or extension of the various embodiments.

[0013] An embodiment of a device 1 for observing skin diseases according to the present invention will now be described with reference to Figure 1. The device comprises the following components: Image acquisition means 11 (e.g. a CMOS image acquisition sensor, preferably with a known focus curve and / or autofocus and / or automatic aperture and / or automatic exposure) configured for acquiring medical images of at least one portion of the skin affected by a skin disease. The acquisition means preferably comprises an optical lens allowing macro-image taking and / or wavelength filtering, as will be further described below. The lens may be fixedly or movably coupled to the optics and the sensor of the acquisition means 11. Sensor means 12 configured to detect a physical condition of a portion of the skin (e.g. thermal camera, electronic nose, humidity sensor, etc.) and / or an environmental condition in the vicinity of said portion of the skin (e.g. a thermometer and / or a hygrometer measuring air temperature and / or humidity) and generate medical condition data representative of said physical condition and / or said environmental condition. · Memory means 13 (e.g. RAM, SSD, HDD memory etc.) for storing data in digital form, e.g. pathology data and / or a set of instructions for carrying out the method for monitoring skin diseases according to the invention. · A processing means 14 (e.g. CPU, GPU, microcontroller, FPGA, etc.) in communication with the image acquisition means 11, the sensor means 12, and the memory means 13. A communication bus 18 allowing data exchange between the processing means 14, the memory means 13, the sensor means 12 and the image acquisition means 11 in wired mode (e.g. via USB) and / or wireless mode (e.g. via Wi-Fi® or Bluetooth®).

[0014] Instead of a communication bus 18, the processing means 14, the memory means 13, the sensor means 12 and the image acquisition means 11 may be connected by a star architecture.

[0015] In the most general embodiment, the processing means 14 (and by extension the entire device 1) is configured to carry out the following steps: reading out the medical condition data detected by the sensor means 12 and the medical images acquired by the image acquisition means 11; storing said medical condition data and said medical images in said memory means 13;

[0016] This allows a dermatologist or non-specialist to use the medical images and medical condition data to make a more accurate diagnosis than would be possible by examining the medical images alone.

[0017] More specifically, device 1 is preferably implemented in two separate preferred embodiments for collecting medical condition data required by dermatologists for diagnosis.

[0018] All embodiments of the device 1 implement the method of skin condition detection according to the present invention.

[0019] Still referring to FIG. 2, the method according to the present invention comprises the following steps. an image acquisition stage P1, in which a medical image of at least one portion of the skin affected by a skin disease is acquired by image acquisition means 11; a physical / environmental data acquisition phase P2, during which a physical condition (e.g. physiological data) of said portion of skin and / or an environmental condition in the vicinity of said portion of skin is detected by the sensor means 12 and pathology data representative of said physical condition and / or said environmental condition are generated; A storage stage P3 in which the medical condition data detected by the sensor means 12 and the medical images acquired by the image acquisition means 11 are read out by the processing means, and said medical condition data and said medical images are stored in the memory means 13.

[0020] This allows physicians to use the medical condition data in conjunction with the medical images to make a more accurate diagnosis than would be possible by examining the medical images alone.

[0021] The first configuration of the device 12, also referred to as the freestanding configuration, preferably comprises a printed circuit board with a crown. The crown has a radius preferably in the range of 2 to 5 cm, and at least three white LED lights, preferably equally spaced along the circumference of the crown, said image acquisition means 11 being located at the center of said crown. In this embodiment, the sensor means preferably comprises at least one of the following configurations of elements for measuring the distance and three-dimensional shape of the lesion, which are arranged according to the criteria described below and in any manner and number that allows the device to detect the distance from the wound and the surroundings (i.e. the skin area around the wound). a) One or more optical (e.g. infrared or laser-light sensors) and / or acoustic (e.g. ultrasonic, also known as PING ultrasonic sensors) distance sensors, which can be arranged in a regular or symmetrical arrangement relative to the image acquisition means 11. The sensors should be located within said image acquisition means 11 or no more than 5 cm away from said image acquisition means 11. b) At least one DLP micromirror sensor arranged in close proximity to the image acquisition means 11, which makes it possible to detect differences in pixel brightness due to fading effects, based on which the processing means can calculate the distance between the image acquisition means 11 and the part of the skin affected by the disease. c) At least one structured light emitter with a corresponding sensor arranged in proximity to the image acquisition means 11, so that the processing means can estimate the depth of the skin lesion based on an image (acquired by the image acquisition means 11) of the portion of the skin illuminated by said structured light. d) A second image acquisition means having an autofocus function (e.g. a CMOS sensor with variable optics), said second image acquisition means being arranged in close proximity to the first means 11. Thereby, the processing means can be configured to estimate the distance based on the focal length of the second image acquisition means without changing the focal length of the image acquisition means 11.

[0022] In other words, the sensor means 12 comprises one or more distance sensors capable of measuring the distance between said portion of the skin and said device, thereby allowing the dermatologist to better evaluate the dermatological pathology shown in the medical image, thus allowing a higher quality diagnosis than if only the medical image was evaluated.

[0023] The device 1 further preferably comprises an internal or removable battery capable of powering the above-mentioned components. Furthermore, the device 1 may be powered by an external power source via a cable and / or by a coil capable of generating sufficient current for the device 1 when a sufficiently high variable magnetic field is passed through it (electromagnetic induction powering method).

[0024] In addition to the above, the device 1 may comprise an interface means (e.g. USB, Bluetooth, IEEE1394 interface, etc.) for communicating with the memory means 13. This allows the images and medical condition data to be accessed by an external computer (e.g. a personal computer used by a dermatologist for reporting purposes). The dermatologist can then conveniently view the images (preferably on a reporting monitor with suitable medical color reproduction) and analyze the medical condition data using a suitable software application package.

[0025] In particular, the interface means may comprise a video interface (e.g. VGA, DVI, HDMI, etc.) connected to a screen (preferably an active matrix display) which may be provided within the device 1 or external to said device 1. This allows the dermatologist to view medical images and pathology data without the need for a dedicated reporting workstation.

[0026] Alternatively or in combination, the device 1 may comprise a communication means, such as, for example, an IEEE802.11 (WiFi), IEEE802.16 (WiMAX), IEEE802.15 (Bluetooth) interface. Furthermore, the processing means 14 may be configured to transmit the acquired medical images and pathology data via said communication means. This eliminates the need to send the device 1 or the memory means 13 of said device 1 to the dermatologist, allowing the dermatologist to make a diagnosis regardless of his location. For example, the dermatologist may be thousands of kilometers away from the location of a competent operator (e.g., a nurse or non-specialist) or of a patient who uses the device 1 to acquire images and pathology data himself. The dermatologist may therefore also interactively (e.g., by video conference) make a diagnosis and suggest to a competent operator or a patient how to use the device 1.

[0027] Further to the above, the processing means 14 of the device 1 may also be configured to acquire images and / or pathology data via said image acquisition means 11 and / or said sensor means 12 based on acquisition data stored in the memory means 13, which specifies how the device 1 should acquire images and / or pathology data. For example, the acquisition data may specify the exposure time and / or aperture to be utilized by the image acquisition means 11, or whether to utilize an autofocus function and / or an exposure adaptation function that automatically adjusts the exposure time, or the wavelength of light to be used to illuminate the skin lesion via the sensor means 12, etc.

[0028] In other words, the memory means 13 may contain at least acquisition data which specifies (in an image acquisition phase P1 and / or in a physical / environmental data acquisition phase P2) how said image acquisition means 11 should acquire images and / or how said sensor means 12 should detect physical and / or environmental conditions. The image acquisition means 11, preferably via the processing means 14, is configured to acquire medical images based on said acquisition data and / or the sensor means 12, preferably via the processing means 14, is configured to detect a physical condition of said portion of skin and / or an environmental condition around said portion of skin based on said acquisition data.

[0029] Furthermore, it may be preferable that the acquired data is generated remotely by a dermatologist using a suitable application and received by the device 1 via communication means.

[0030] This reduces the risk of errors by the dermatologist due to incorrect use of the device 1. Thus, the dermatologist can make a more accurate diagnosis since the risk of errors due to incorrect acquisition of images is reduced. Indeed, if the dermatologist cannot communicate with the patient or operator due to language issues (e.g., because the user of the device 1 does not speak the same language as the dermatologist) and / or cultural differences (e.g., because the user of the device 1 does not understand what the dermatologist is saying and / or is unable to configure the device 1), the device 1 can be configured to send the appropriate acquisition data to ensure proper operation.

[0031] Alternatively or in combination, the sensor means may comprise at least one sensor capable of detecting a physiological parameter of the patient. The sensor may be connected to the board (and therefore also to the processing means 14) by a suitable connector or may be directly integrated (for example by soldering) in the printed circuit. These sensors include the following types: · an ultraviolet image sensor capable of detecting light with wavelengths in the range of 200 to 450 nanometers and one or more ultraviolet light sources (e.g. Wood's lamps, also known as UV lamps), which allow the detection of the fluorescence of some bacteria or virus species. The sensor may also be implemented by applying polarized optics (with fixed or removable frame) to the image acquisition means 11 in order to filter the light so that only light with wavelengths below 480 nm can be transmitted, in order to eliminate any signal crosstalk phenomenon. This allows the dermatologist to make a higher quality diagnosis. an infrared imaging sensor, for example an NIR CMOS camera, capable of detecting light having a wavelength in the range of 650 to 1,200 nanometers, and one or more infrared light sources, preferably of the "near" infrared type (NIRS), capable of emitting light rays with wavelengths in the range of 650 to 1,200 nanometers. Said infrared imaging sensor may preferably operate with a sampling frequency of at least 100 Hz and is preferably equipped with polarized optics (with fixed or removable frame) calibrated for the same wavelengths (650 to 1,200 nanometers). This allows the dermatologist to make a higher quality diagnosis, since there is no signal crosstalk phenomenon. At least one electronic nose configured to recognize volatile compounds emitted by said portions of skin affected by a skin disease (e.g. the presence of ammonia, which typically indicates the presence of an ulcer, and / or other volatile compounds emitted in the presence of certain bacterial species, as further described below), thus allowing the dermatologist to make a higher quality diagnosis, i.e. allowing the dermatologist to examine the patient's skin in person and (remotely) detect the presence of substances that are barely detectable by the human nose during the dermatologist visit. One or more heat-sensing devices (e.g., infrared thermometers, thermal cameras, etc.), also called temperature sensors, positioned to detect the surface temperature of the area of ​​skin affected by the skin condition. at least one electronic environmental humidity and temperature sensor, and / or a skin humidity sensor capable of detecting humidity at said portion of the skin affected by the skin disorder, which allows the dermatologist to better assess the appearance and condition of the skin disorder than if the image alone was evaluated, thus allowing a higher quality diagnosis;

[0032] Alternatively or in combination with the above, the device 1 comprises a data input interface (e.g. a keyboard, a touch screen capable of displaying a keyboard, a microphone, etc.) which can communicate with the processing means 14 and be configured to obtain patient data (e.g. a photographic representation of the patient's skin, the patient's gender, age, weight, etc.) and store it in the memory means 13.

[0033] This provides dermatologists with a larger amount of data, allowing them to make higher quality diagnoses.

[0034] Alternatively or in combination with the above, the sensor means may comprise a myoelectric unit comprising the following elements: Preferably 0.5mm 2 A pair of skin electrodes for collecting electrical surface biosignals, of the dry disk or silver chloride (AgCl) type, having a surface area of ​​at least 10 mm. A signal generator configured to generate an alternating current electrical signal, preferably having a sine or square wave, adjustable up to a maximum of 40 Volts peak-to-peak (Vpp) and with a maximum frequency of 1 Hz, so as to effectively generate a current having an intensity of 50 μA or less, or, if the patient is using a cardiac catheter, an implantable pacemaker, or a cardioverter-defibrillator (also known as an ICD), an intensity of 10 μA or less to effectively avoid any risk of microshock. Measurement means, e.g. an ammeter and / or a voltmeter, capable of acquiring the myoelectric signal (e.g. by sensing one or more characteristics thereof such as intensity, voltage, phase and / or frequency) generated by the current flowing through the pair of electrodes and transmitting said myoelectric signal, preferably in digital form, to the processing means 14.

[0035] This allows the dermatologist to make a higher quality diagnosis since the electromyographic signal can be used to provide meaningful information about the state of the surrounding nervous system, whose behavior may be affected by the presence of certain skin disorders. Of course, many variations on the above example are possible.

[0036] A second embodiment of the device according to the invention is described below. For the sake of brevity, the following description focuses only on those parts that distinguish this embodiment and subsequent variations from the main embodiment described above.

[0037] A second embodiment of the device according to the invention is a general-purpose device such as a mobile terminal (e.g., a smartphone), a tablet, a notebook computer, a personal computer, etc., to which the image acquisition means and the sensor means described above can be connected via an input / output means (e.g., via USB, Bluetooth, etc.), said device being configured to execute a set of instructions implementing the method for monitoring skin diseases according to the invention.

[0038] Still referring to FIG. 3, a first neural network RP is capable of analyzing an image P showing a first portion P2 of a lesional skin, preferably affected by a skin disease, and a second portion P3 (also known as the "periphery") surrounding the lesion P2. The first neural network RP is preferably of the feed-forward type, implemented by a processing means 14, or by other processing means (e.g. contained in a server, a personal computer of the dermatologist, etc.) which preferably executes a set of instructions implementing said neural network RP. This neural network RP is trained to output typology data defining a typology of a skin disease, based on the images acquired (entered into said network). Using this typology data, the dermatologist can thus speed up the diagnostic process, since it allows the dermatologist to narrow the scope of the examination in a short time, avoiding other speculative types of examinations that may be performed on the patient. Thus, a faster diagnosis (and therefore treatment) is made. This is appropriate in the context of the use of the device 1 of the invention. That is, the dermatologist may need to analyze images and pathology data collected by different devices 1 in the field and therefore needs to provide a diagnosis as quickly as possible.

[0039] The neural network RP preferably has two layers of computation nodes S p1 , S p2 (hereinafter, "synapse s" S p1_1 , ..., S p1_n and S p2_1 , ..., S p2_n (The number of layers depends on the number of lesion types to which the analyzed image may correspond.) p1 and S p2 Synapse S in each p1_1 , ..., S p1_n and S p2_1 , ..., S p2_n It should be understood that the number of synapses must be the same. The synapses of each layer Sp1 and Sp2 are divided into three vectors (first layer S p1 s forpr_1 , s pg_1 , s pb_1 and the second layer S p2 s for pr_2 , s pg_2 , s pb_2 ) respectively.

[0040] It is emphasized that in the absence of the neural network RP, the user must input the lesion type via interface means.

[0041] 4 and 5, the data detected by the sensor means 12, the use of which will be described below, may be processed by means of a second neural network R1 and a third neural network R2, each of which may be implemented through the processing means 14 of the device 1 or other processing means (e.g. contained in a server, in the dermatologist's personal computer, etc.) (completely analogous to the first neural network RP).

[0042] The function of the second neural network R1 is to process the data collected directly by the sensor means 12, thereby obtaining the inflammatory and / or infectious state of the lesions that may have been caused by a disease, whereas the third neural network R2 is a network designed to process the data generated by R1 in order to calculate the degree of infection and / or inflammation, and can be added to a device or system if one wishes to read and interpret the generated data.

[0043] The second network R1 is preferably a neural network of the perceptron type, designed to perform deep learning operations. Said network R1 comprises at least five vectors SR1, each comprising a computation node or synapse. A central vector S0 is a vector Z whose values ​​z1, ..., z2 are input to the neural network SR1. n0The layer S0 performs the task of receiving a value from the above-mentioned sensor means 12 included in the configuration of the device. The number of synapses in the layer S0 is defined based on the type of the sensor means 12 included in the device. Specifically, the number of synapses is preferably selected based on the following formula (adding up the number of synapses that can be associated with each sensor means 12 included in the device 1): If the sensor means 12 comprises a UV light source, the number of synapses that the neural network SR1 has may range from 1 to 6. When the sensor means 12 comprises an infrared light source, the number of synapses that the neural network SR1 has may be at least one. If the sensor means 12 comprises an electronic nose, the number of synapses that the neural network SR1 has may range from 1 to x, where x is the number of volatile compounds recognizable by the electronic nose. When the sensor means 12 includes one or more temperature sensors, the number of synapses that the neural network SR1 has may be at least one. If the sensor means 12 comprises one or more electronic humidity sensors, the number of synapses of the neural network SR1 may be at least one.

[0044] Layer S0 is the first line layer S 1-i、 S 1-h The first line layer S 1-i is preferably dedicated to the detection of an infectious condition, while the second first line layer S 1-h are preferably dedicated to the detection of inflammatory conditions, each having the same number of synapses as layer S0. 1-i and S 1-h Each of the layers has at least one further layer S, called the second-line layer. 2-i and S 2-h The second line layer S 2-i , S 2-h The number of synapses in is determined by the number of synapses in layer S0 and therefore the number of synapses in the first-line layer S 1-i、S 1-h Consider the number of synapses in S0, N0, which is equal to the number of synapses in each first-line layer, N1, then 2-i The number of synapses, N2, of the layer is N1 (N1-1). The same rule applies to any other layer following the second line one. X Let N be the number of synapses. X As a line of layers for which we need to calculate N, the following relationship holds: X =N X-1 (N X-1 -1)

[0045] The connections between the synapses of layers S0 and S1 are simple; that is, each synapse of layer S0 is connected to only one synapse of layer S1. The connections between the synapses of layers S1 and S2 are made such that each synapse of the previous layer is a common connection with all other computational nodes of that layer in the next layer. For any other layers following the one in the second line, the connections between their synapses and the synapses of the previous layer must follow the same logic as the previous layer. Each layer of the network has an operational output O X-i , ..., O0, ..., O y-h Generate.

[0046] Thus, at least five values ​​are generated that can be read out to verify lesion infection status, lesion inflammation status, or both.

[0047] In other words, the processing means 14 or the external processing means may be configured to execute a portion of code implementing a second neural network R1 trained to generate disease status data defining whether said portion of the skin affected by said skin disease is infected and / or inflamed based on said disease status data. This allows the dermatologist to make a higher quality diagnosis in the same time, since he does not have to reinterpret the data from the sensor means 12 each time there is a need for a diagnosis (possibly also consulting some medical textbooks), but can instead have this portion of processing performed by a second neural network that he has preferably supervised and that he has trained to provide one or more possible solutions that the dermatologist then needs to interpret.

[0048] The output values ​​of the second neural network R1 are then read by a third neural network R2 whose function is to evaluate the degree of inflammatory or infectious state of the lesion under examination, as described above. Neural network R2 correlates the values ​​provided by network R1 with the myoelectric signals E generated by the myoelectric unit or with patient data obtained via the data input interface.

[0049] In other words, the processing means 14 may be configured to execute a portion of code implementing a third neural network R2 trained to generate symptom extent data defining the degree of infection and / or inflammation of the portion of the skin affected by the skin disease based on the pathology status data (generated by the second neural network R1) and the electromyographic signal E (generated by the electromyographic unit).

[0050] This allows the dermatologist to make a higher quality diagnosis in the same amount of time, since he does not need to reinterpret the data from the sensor means 12 each time a diagnosis is needed, but instead has part of this processing carried out by a third neural network, preferably supervised by him, which he has trained to provide one or more possible solutions that the dermatologist then needs to interpret.

[0051] The third neural network R2 has at least five synapses s r-1 , ..., s r-q and additional synapses s e A single-layer feedforward network s formed by r Each operational output O of the second neural network R1 is connected to a synapse s r-1 , ..., s r-q Therefore, if we increase the number of layers in the neural network R1, the number of synapses s r-1 , ..., s r-q The number of synapses s must be increased by the same amount. r-1 , ..., s r-q Each node S S1 , S S2 Synapses e The first node S to which S1 is the adder node. On the other hand, the second node S S2 is a relation node, the operation of which will be explained later.

[0052] The aim of both embodiments of the device 1 described herein is to detect both the type of lesion (if the neural network Rp is present) and the possible inflammatory and / or infectious state of the skin lesion, optionally further determining its extent. This is carried out using images acquired by the image acquisition means 11 and data acquired by the sensor means 12 and subsequently processed by the networks R1 and optionally R2. Each of them first undergoes a training phase using the same data generated by the image acquisition means 11 and / or the sensor means 12, depending on the network involved. This phase calibrates the values ​​associated with the synapses of all layers constituting the neural networks RP, R1 and R2. Once the training phase is completed, the network can move to an operational phase, in which the network can autonomously process the data collected by the image acquisition means 11 and the sensor means 12. In this phase, the values ​​associated with the synapses remain unchanged. Several training phases can be carried out, for example each time a synapse is added to a network layer or each time a layer is added to the network R1. A new training phase may be initiated if the network is deemed not to have an adequate level of processing sensitivity or agility, it being understood that in the absence of a network RP in the system, the operator must input the type of lesion under examination via the device's data entry interface.

[0053] When the device 1 is in an operational state, said device is directed towards the lesion parallel to the plane of the relevant body area. This state is preferably achieved by the use of image acquisition means, which may contribute to the operator to localize the appropriate skin lesion. After this, at least one picture of the lesion and the surrounding skin area, which is used for reference, is acquired by the image acquisition means 11. This is preferably done by using the light generated by at least one of the crown LEDs. After this picture is taken, the acquired image must be collected and stored in the memory means 13 of the device 1. At the same time or after the picture is taken, physiological data of the lesion are also collected by the sensor means 12. Said data must preferably be digitized in order to be read by the network R1. Each of said physiological data is considered as an input z to the network R1. Each input is associated to one node of the layer S0 that receives it.

[0054] As mentioned above, some examples of uses of the physiological data collected by each type of sensor means 12 are precisely described below along with the associated methods of obtaining said physiological data.

[0055] When the sensor means 12 includes an ultraviolet image sensor and one or more ultraviolet light sources as described above, images can be obtained that allow the presence and amount of some bacterial or viral species to be detected (preferably with the aid of optical filters). Below is a list of some skin diseases that result in skin lesions that reflect ultraviolet light and produce light of specific colors: Tinea capitis (fungal infection), emits blue / green light, Tinea versicolor (fungal infection), emits yellow / green / orange light, Erythrasma (bacterial infection), emits red light, White spots (cell death), emitting white light, Porphyria cutanea tarda (a medical condition that is hereditary or caused by certain environmental conditions), which emits red / pink light; Pseudomonas aeruginosa infection (bacterial infection), emits green light, Acne (bacterial infection), emits orange / red light, Corynebacterium minutissimum (bacterial infection), emitting red light.

[0056] The acquired image is preferably coded using RGB code and analyzed pixel by pixel, taking into account the part of the image that contains the lesion and the small skin area adjacent to it (surrounding it). Each detection of a color (yellow, green, blue, red, orange, pink, a total of six colors) is associated with a specific input vector Z. Thereby, as described above, there can be up to six synapses associated with the UV light source, depending on the number of colors the system is designed to detect and calculate. For each value, the ratio between the pixels of the image with that particular color and all the pixels of the image part under examination is calculated. If the ratio exceeds a threshold value (preferably adjustable, the minimum is preferably 0.5%), the z associated with that color will be 1, otherwise it will be 0.

[0057] If the sensor means 12 comprises an infrared image sensor and one or more infrared or "near" infrared (NIRS) light sources, it is possible to detect the amount of oxygenated hemoglobin relative to deoxygenated hemoglobin. This value indicates the physiological activity of the body area containing the lesion, and therefore its possible inflammatory state. The illumination of the IR or NIR diodes can be activated from a minimum distance of 1 cm. By illuminating the area and surroundings of the lesion with IR or NIR light of wavelengths ranging from 760 nm (maximum absorbance by deoxygenated hemoglobin) to 900 nm (maximum absorbance by oxygenated hemoglobin) and detecting the output signal with a NIR CMOS camera, it is possible to count the number of points representing parts of the skin absorbing at 760 nm wavelength and parts absorbing at 900 nm wavelength. Thus, the respective quantities of both within the area of ​​interest can be recognized. It is to be appreciated that, according to the Beer-Lambert law, the calculated absorption (A) of IR or NIR light for each wavelength is: A=log(I inc / I ril ), where I inc = incident light, I ril = detected light.

[0058] (A 900nm -A760nm ) / A 760nm is greater than or equal to a given adjustable threshold, the lower limit of which is preferably set to 0.1%, then the input to the effective synapse of layer S0 will be 1.

[0059] When the sensor means 12 comprises at least one electronic nose as described above, the device 1 is able to detect the presence of one or more volatile compounds such as: Ammonium, B. Isoprene, C. acetic acid, D. Ethanol, e. dimethyl disulfide, f. Hydrogen sulfate (sulfuric acid), G. Acetone, h. acetaldehyde, i. methyl thiocyanate, j. Hydrogen cyanide, K. Formaldehyde, l.butanol.

[0060] These volatile compounds are from various viral species that can be found in the area of ​​skin lesions. Such species include Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The bacterial and viral flora that can be found by volatile compound detection is very large. And it has been confirmed that the range of volatile compounds that are produced and therefore detectable by the electronic nose is the same. The electronic nose(s) included in the system must be configured to detect the presence of at least one volatile compound that can be scientifically linked to a bacterial or viral element. The presence of each volatile compound is associated with an input z that is read by a specific synapse of the layer S0 of the second neural network R1. For each compound to which the electronic nose(s) is configured to respond, an adjustable threshold is set. If the detection of the specific compound exceeds the threshold, the value of z obtained is 1, and if not, it is 0.

[0061] When the sensor means 12 includes one or more heat sensing devices as described above, it is possible to detect the temperature in the area of ​​the lesion and in adjacent areas. A higher temperature in the area of ​​the lesion than in the surrounding area indicates the presence of an inflammatory condition. F is the average temperature in the damaged area, T P is the average temperature in the area around the wound, and the ratio (T F -T P ) / T P However, if the minimum limit exceeds an adjustable threshold, preferably set to 0.1%, then the input z associated with a particular synapse of layer S0 of the second neural network R1 associated with the heat sensing device preferably has a value of 1, and if not, has a value of 0.

[0062] If the sensor means 12 comprises an electronic humidity sensor capable of detecting the presence and amount of exudate in the area of ​​the lesion, it is detectable whether the value returned by the humidity sensor exceeds an adjustable threshold (minimum limit is above 0), and preferably a value of 1 if the humidity value exceeds said threshold, or a value of 0 if it does not, is sent as input Z to a specific synapse of the layer S0 of the second neural network R1.

[0063] As mentioned above, the number of inputs Z sent from the sensor means 12 to the network R1 depends on the number, type and configuration of said sensor means 12 in a given configuration of the device 1 or system. The number of inputs z must match the number of synapses in layer S0 of the network R1. For each detection, an input z vector is generated and designated as Z that is sent to layer S0 of the second neural network R1. Depending on the configuration of the sensor means 12, each z is always sent to the same synapse.

[0064] It should be understood that the method of acquiring pathology data and the resulting generation of vector Z does not depend on whether neural networks R1 and R2 are in a training or operational phase. The same is true for the image acquisition method, i.e., it does not change depending on whether first neural network RP is in a training or operational phase.

[0065] The operation of the first neural network RP when said network RP is in the training phase is described below. As mentioned above, the network consists of two layers of synapses (S p1 and S p2 ) The number of synapses in each layer, S p1_1 , ..., S p1_n and S p2_1 , ..., S p2_n depends on the number of lesion types that the network RP needs to recognize after training.

[0066] Each synapse has three synaptic vectors s pr_1 , s pg_1 , s pb_1 and s pr_2 , s pg_2 , s pb_2 A vector position value consisting of an index ranging from 1 to 256 is connected to both layers S p1 and S p2 Each synapse vector s pr_1 , s pg_1 , s pb_1 and s pr_2 , s pg_2 , s pb_2 If this is the first time the network is trained, all the synapses s pr_1 , s pg_1 , s pb_1 and s pr_2 , s pg_2 , s pb_2 is initialized to a value of 0.

[0067] Once the picture has been taken, the user, preferably via a data entry interface, must draw a closing line on the image, i.e. the drawn line overlaps the pixels in the captured image that represent the edge of the lesion. From a computational point of view, the user may select a region of interest (also called ROI), i.e. a portion of the image that includes the lesion and the surrounding area. In such a case, the network RP is trained considering the ROI as the captured image. Before or after this, the user must preferably also make use of the data entry interface to select the lesion type under examination from a list of lesion types that the network has been trained to recognize. Each selectable lesion type is associated with a pair of synapses. Once the line has been drawn, all the pixels within the contour (called px1) and all the pixels of the image outside the selected contour (called px2) are analyzed by reading out their respective R, G, B values. The R, G, B values ​​of each pixel must be within the range of 0 to 255.

[0068] If the analysis pixel belongs to the group of pixels px1 and its R, G, and B values ​​are r, g, and b, respectively, then the layer S corresponding to the lesion selected by the user is p1 Synapse S p1_1 , ..., S p1_n , synapse S at position r+1 pr_1 , S at position g+1 pg_1 , S at position b+1 pb_1 is increased by 1 unit. If the analysis pixel belongs to the group of pixels px2 and its R, G, and B values ​​are r, g, and b, respectively, then the layer S corresponding to the lesion selected by the user is p2 Synapse S p2_1 , ..., S p2_n , synapse S at position r+1 pr_2 , S at position g+1 pg_2 , S at position b+1 pb_1 is incremented by one unit. After all images have been analyzed, the synapse s pr_1 , s pg_1 , s pb_1 and s pr_2 , s pg_2 , s pb_2 is normalized.

number

[0069] The neural network RP performs a two-layer S p1 and S p2 All synapses in S p1_1 , ..., S p1_n and S p2_1 , ..., S p2_n Once a number of images equal to or greater than the sum of

[0070] Once the neural network RP has been trained, the image acquired by the image acquisition means 11 is analyzed pixel by pixel. Before the analysis begins, it is necessary to program a neighbouring value v, which must be an odd number. The neighbouring value v can range between 1 and the total number of pixels in the image under examination. The analysed pixel is placed in a structured lattice formed by v2 pixels. The remaining elements of the lattice are filled with the pixels around the pixel under analysis. For each pixel in the lattice, the values ​​of R (=r), G (=g) and B (=b) are analysed. At this point, the layer S p1 and layer S p2 The pair of synapses corresponding to lesions in both pr_1 and S pr_2 , S at position g+1 pg_1 and S pg_2 , and S at position b+1 pb_1 and S pb_2 The values ​​of are compared and the value F is calculated according to the following formula (1 is the layer S p1 and S p2 (Indicates pixel sp corresponding to a given lesion typology in

number

[0071] Each synapse S corresponding to each lesion type p1_1 , ..., S p1_n and S p2_1 , ..., S p2_nFor each pixel in the grid, a value F for each pixel is calculated. Then, all the values ​​F of all pixels in the grid for a given lesion type are summed. If a negative value is obtained for a given lesion type, the central pixel in the grid is classified as healthy for that lesion. If a positive value is obtained, the central pixel in the grid is classified as positive for that given lesion.

[0072] For each lesion type, the positive pixels are summed to obtain the extension of the given lesion type. The extension value can be converted to a metric system based on the distance of the device 1 from the lesion detected by the optical and / or acoustic distance sensor(s). The distance value thus obtained is compared with the focal curve of the CMOS camera to obtain the area covered by a single pixel of the image. This value is multiplied by the pixels belonging to the given lesion type to obtain the extent of the skin lesion.

[0073] From this description it is clear that the neural network RP cannot replace the diagnostic task of the dermatologist, but can support him in his work by highlighting to him different skin diseases that may appear in the images acquired by the image acquisition means 11. This contributes to a higher quality diagnosis (in a shorter time) by the dermatologist.

[0074] The training phase for the third neural network R2 is described below. For this purpose, the network R1 is made up of a layer S0 of synapses and a layer S1 of synapses. j-i (j ranges from 1 to x) and the y-layer of synapses S k-hIt should be understood that the layer of synapses i is at this stage "trained" to calculate, from the data from the sensor means 12, the presence of an infectious state of the lesion. Meanwhile, the synapses h are used to calculate the presence of an inflammatory state. The number of synapses in S0 is generally equal to a number N0, which depends, as mentioned above, on the number and type of sensor means 12 provided (or connected) in the particular configuration of the device 1. As mentioned above, the synapses of layers i and k are connected to those in layer S0 and other layers. All synapses of the network can take values ​​between 0 and 1, which are discretized into a number of intermediate values ​​called modifiable steps. The number of steps that the synapses of a given layer can take cannot be less than the number of synapses present in that layer. At the beginning of the training of the third neural network R2, the value of all synapses is set to a value of 0, regardless of the layer to which it belongs. Between a layer of synapses and the next, an adjustable threshold θ is set, ranging from 0 to 1. In the networks considered here, the x threshold θ j-i (j ranges from 1 to x) and y threshold θ k-h (k ranges from 1 to y) are set. θ must be the same value 1-i and θ 1-h Except for , all thresholds θ can be different from each other.

[0075] It should be appreciated that for each acquisition, a vector Z is generated, composed of N0 inputs z, each of which is input to a particular one of the N0 synapses of layer S0. Also, for each acquisition, a user U, preferably a dermatologist, preferably via a data input means, inputs a desired output (o i and h ), which must be set to 1 if an infectious state of the lesion or an inflammatory state of said lesion is present, respectively. The presence of an infectious or inflammatory state is determined by the operator during the training phase. For each synapse in layer S0, at each acquisition, a weight w0 is calculated as follows:

number

[0076] where n0 is an integer ranging from 1 to N0. The weights obtained in the layer S0 are summed to obtain the calculated output value Σ0, which is then used to calculate the stable value Δ1 according to one of the following formulas:

number

[0077] where Θ is the Heaviside function. The Heaviside function Θ(x) returns an inactive value for negative x and a single value for positive x. If Δ1 is greater than 0, the weights of the synapses in layer S0 are unchanged. If Δ1 is less than or equal to 0, then Θ(o i +o h ) is equal to 1, the weight of the synapse with an input equal to 1 is increased by one. On the other hand, when Θ(o i +o h ) is equal to 0, the weights of synapses with input equal to 1 take on a value one step lower.

[0078] If Δ1 is greater than 0, the input value z is 1-i and S 1-h These two layers are trained in the same way as already applied to layer S0. 1-i For, the weights are calculated as follows:

number

number

number

[0079] Σ 1-i and Σ 1-h The calculated output values ​​of Δ 2-i and Δ 2-h Meanwhile, the values ​​of the synapses of each layer are modified in the same way as applied to the synapses of layer S0.

[0080] The training method is repeated for the layers following the first line one. In general, the weights of each synapse are calculated as follows:

number

number

[0081] However, it should be understood that the input to each synapse is variable. 1-i and S 1-h Each synapse in the layer following S is connected to at least two synapses in the previous layer. u The general synapse of the previous layer S 1(u-1) and S 2(u-1) Considering two synapses, the input z u is Θ((w 1(u-1) +w 2(u-1) )-1). Two values ​​w 1(u-1) and w 2(u-1) are the weights of each of the two synapses in the previous layer. If the stable value is greater than 0, the next input value sent from the previous layer is generated. Otherwise, all inputs sent to the next layer should be considered invalid.

[0082] When a number of input vectors Z at least equal to the total number of synapses contained in the second network R1 have been sent to layer S0, the network R1 may be considered trained.

[0083] During the operational phase, the second neural network R1 consists of synapses with weights obtained during the previous training phase. During this phase, neither the values ​​nor the network structure change. However, θ 1-i and θ 1-h If the values ​​of θ are programmed to be equal, then it is possible to reprogram the threshold θ for each layer.

[0084] In this stage, the input values ​​sent to the individual layers are obtained in the same way as during the training stage. During the acquisition of pathological condition data via the sensor means 12 of the device 1, the user i and h The values ​​of θ are not defined. The pathology data generated by the sensor means 12 are input to the layer S0. As in the training phase, for each layer, the values ​​of the synaptic weights are calculated as follows:

number

number

[0085] O b If exceeds 0, the inputs to the next layer are generated in the same way as in the training phase. If it does not, all inputs sent to the next layer are invalid. When a new acquisition is made, the network R1 is 1-i If >0, the lesion is confirmed as infected and O 1-h If >0, inflammation is confirmed to be present.

[0086] The training phase for the third neural network R2 is described below. As mentioned above, the purpose of neural network R2 is to assess the degree of infection and / or inflammation of a lesion by a circuit model of human skin, which from an electrical point of view is similar to a memristor, i.e., a magnetic flux

number

number

number

[0087] In the above relation, R is the electrical resistance of the part of the skin under examination (proportional to the average resistance of the skin and to the size of the part under examination), D is the depth of the skin and c0 is a constant. The value α is related to the electrical potential and to the presence of ions in the pores of the surface of the skin, while τ depends on the morphological characteristics of the skin. A third neural network R2 makes it possible to link infection and inflammation (ascertained by network R1) to α, since the presence of an inflammatory or infectious condition can modify these conditions. The training phase for network R2 can only be carried out with the aid of network R1 of the operational phase.

[0088] As mentioned above, network R2 has a single layer S r This layer is composed of synapses whose number is one less than the number of layers that make up network R1. This is because the operational output value of layer S0 is not input to the network. r Consider a network R1 formed by layers. The inputs sent to the nodes are the operational output values ​​O q where q is from 1 to n r It is an integer in the range of -1. rEach synapse in can take a real value ranging from -1 to 1. The network further includes additional synapses, which during a training phase can be associated with values ​​calculated based on the EMG signal or empirically determined. In the former case, an electrical signal is preferably applied at each acquisition, according to the above mentioned specifications (to prevent any body shock to the patient), by dry disk electrodes placed at a maximum distance of 5 cm on either side of the lesion under test. The applied voltage flux and the current charge detected by the electrodes are then calculated. A circuit model of the skin-equivalent memristor then obtains the value of α and the synapse s for that acquisition is then calculated. e Synapse s e Once the value of is empirically determined and entered via an interface means, the node s e can take on a user-determined value ranging from 0 to 10. The user increases this value as the level of infection and / or inflammation increases.

[0089] During the training phase, node s e When the value of α obtained by acquiring an electrical signal is assigned to the value of α, it is necessary to obtain the basic value α0 in advance. This is because when the lesion is neither inflammatory nor infectious, the node s constituting the neural network R2 r The value of α can be obtained by averaging the values ​​of α obtained through the acquisition of a number of electrical signals equal to or greater than the number of s. e When the assignment to a value is done heuristically, the base value is set to 0.

[0090] Once the value of α0 has been determined by the signal acquisition method, we can move on to training the network with the operational output values ​​from the layer of the second neural network R1. r All synapses in e During the first acquisition of the first training stage, all synapses s r-1 , ..., s r-q are set to a value of 1 and do not change their values. For each acquisition, for each synapse, the weights are calculated as follows:

number

[0091] Synapse e If the assignment of values ​​of is done empirically (i.e. by dermatologist U), then with each new acquisition, it is necessary to assign to that synapse a value proportional to the degree of infection and / or inflammation of the observed lesion. In this case too, at the beginning of the training phase, the layer S r All synapses in have a value of 1. r The method for changing the value is the same as in the previous example.

[0092] Layer S r The training of network R2 may be considered complete when at least a number of acquisitions equal to the number of synapses of

[0093] When network R2 is in the operational phase, layer S rThe synaptic values ​​of s are obtained from the training phase described above and do not change during the movement. In this phase, there is no acquisition of electrical signals E by the myoelectric units or synaptic s e There is also no assignment of an empirical value α for α, since here only the processing of output data obtained from the network R1 after the acquisition that has already been performed is provided. This is possible because during the operational phase, the memristor model is considered fully calibrated and no further adjustments are required.

[0094] As in the training phase, each time we obtain the output values ​​of the second neural network R1, we also obtain the output values ​​of the layer S1, except for those obtained from the layer S0 of the second neural network R1. r At this stage, the synaptic weights are also obtained as follows:

number

number

[0095] The value of q(t) is calculated by the flow Φ(t) obtained from a simulated sinusoidal voltage wave signal with a frequency of 1 Hz and a peak-to-peak value of 40 volts. A curve obtained therefrom is then drawn. Based on said curve, from its shape, the degree of infection or inflammation of the lesion under examination is obtained. For this purpose, the processing means 14 may be configured to determine the degree of infection or inflammation of the lesion based on the flow Φ(t). Alternatively, the flow Φ(t) may be displayed on the dermatologist's terminal and / or on visualization means (e.g. active matrix display, 3D glasses, etc.) connected to the video interface of the device 1.

[0096] While this specification has addressed some of the possible variations, it will be apparent to those skilled in the art that other embodiments may be implemented. Some elements may be replaced with other technically equivalent elements. The present invention is therefore not limited to the illustrative examples described herein, but may be subject to numerous modifications, improvements, or substitutions of equivalent parts and elements without departing from the basic inventive concept as set forth in the following claims.

Claims

1. A device for observing skin diseases, comprising image acquisition means adapted to acquire medical images of at least one portion of the skin affected by a skin disease, - sensor means configured to detect a physical condition of said portion of skin and / or an environmental condition surrounding said portion of skin and generate medical condition data representative of said physical condition and / or said environmental condition; memory means for storing data in digital form; processing means in communication with said image capture means, said sensor means, and said memory means; The processing means further comprises: reading out the medical condition data detected by the sensor means and the medical image acquired by the image acquisition means; configured to store said medical condition data and said medical images in said memory means; The sensor means comprises: one or more distance sensors capable of measuring the distance between the portion of skin and the device; and / or an ultraviolet image sensor capable of detecting light having a wavelength in the range of 200 to 450 nanometers, and one or more ultraviolet light sources; and / or an infrared image sensor capable of detecting light having a wavelength in the range of 650 to 1,200 nanometers, and one or more infrared light sources; and / or one or more thermal sensing devices capable of detecting a surface temperature of the at least one portion of skin affected by the skin disease; and / or at least one humidity sensor capable of sensing humidity at the portion of skin affected by the skin disease; The sensor means includes a myoelectric unit capable of acquiring a myoelectric signal. device.

2. The device of claim 1 , wherein the sensor means comprises an electronic nose configured to detect the presence of volatile compounds emitted by the at least one portion of skin affected by the skin disease.

3. 3. The device of claim 1 or 2, wherein the memory means includes at least acquisition data specifying how the image acquisition means should acquire the medical image and / or how the sensor means should detect the physical condition and / or the environmental condition, and the image acquisition means is configured to acquire medical images based on the acquisition data and / or the sensor means is configured to detect the physical condition of the portion of skin and / or the environmental condition surrounding the portion of skin based on the acquisition data.

4. A device according to any one of claims 1 to 3, comprising communication means for communicating with a processing device.

5. The processing means includes: receiving the acquired data via the communication means; Storing the acquired data in the memory means 5. The device according to claim 3 or 4, configured so as to

6. 6. The device of claim 1 , wherein the processing means is configured to execute a first portion of code implementing a first neural network trained to generate typology data defining a typology of the skin disorder based on the medical images.

7. The sensor means includes the ultraviolet image sensor, and the one or more ultraviolet light sources; the infrared imaging sensor and the one or more infrared light sources; and the one or more heat sensing devices; and at least one of the at least one humidity sensor; 7. A device as claimed in any one of claims 1 to 6, wherein the processing means is configured to execute a second portion of code implementing a second neural network trained to generate disease condition status data defining whether the portion of the skin affected by the skin disease is infected and / or inflamed based on the disease condition data.

8. 8. The device of claim 7, wherein the processing means is configured to execute a third portion of code implementing a third neural network trained to generate symptom extent data defining a degree of infection and / or inflammation of the portion of skin affected by the skin disease based on the disease condition status data and the electromyographic signal.

9. 6. A system comprising the device of claim 4 or 5 and a processing device, the device being configured to transmit the medical image to the processing device via a communication means, the processing device being configured to execute a first portion of code implementing a first neural network trained to generate typology data defining a typology of the skin disorder based on the medical image.

10. A system comprising a device according to claim 4 or 5 and a processing unit, The sensor means includes the ultraviolet image sensor, and the one or more ultraviolet light sources; the infrared imaging sensor and the one or more infrared light sources; and the one or more heat sensing devices; and at least one of the at least one humidity sensor; The system, wherein the device is configured to transmit the medical condition data to the processing unit via a communication means, and the processing unit is configured to execute a second portion of code implementing a second neural network trained to generate medical condition status data defining whether the portion of the skin affected by the skin disease is infected and / or inflamed based on the medical condition data.

11. The system of claim 10, wherein the processing device is configured to execute a third portion of code implementing a third neural network trained to generate symptom severity data defining a degree of infection and / or inflammation of the portion of skin affected by the skin disease based on the disease condition status data and the electromyographic signal.

12. 1. A method for observing a skin disease comprising an image acquisition step, in which a medical image of at least one portion of skin affected by a skin disease is acquired by an image acquisition means, the method comprising: a physical / environmental data acquisition stage, in which a physical condition of said portion of skin and / or an environmental condition surrounding said portion of skin is detected by sensor means and medical condition data representative of said physical condition and / or said environmental condition is generated; a storage step in which the disease condition data detected by the sensor means and the medical image acquired by the image acquisition means are read by a processing means, and the disease condition data and the medical image are stored in a memory means; Further equipped with The sensor means comprises: one or more distance sensors capable of measuring the distance between said portion of skin and said image acquisition means; and / or an ultraviolet image sensor capable of detecting light having a wavelength in the range of 200 to 450 nanometers, and one or more ultraviolet light sources; and / or an infrared image sensor capable of detecting light having a wavelength in the range of 650 to 1,200 nanometers, and one or more infrared light sources; and / or one or more thermal sensing devices capable of detecting a surface temperature of the at least one portion of skin affected by the skin disease; and / or at least one humidity sensor capable of sensing humidity at the portion of skin affected by the skin disease; The method, wherein the sensor means includes an electromyographic unit capable of acquiring an electromyographic signal.

13. 13. The method of claim 12, wherein in the image acquisition step and / or the physical / environmental data acquisition step, the medical image of the portion of skin and / or the physical condition and / or the environmental condition around the portion of skin are acquired based on acquisition data specifying how the image acquisition means should acquire the medical image and / or how the sensor means should detect the physical condition and / or the environmental condition.

14. A computer program loadable into the memory of an electronic computer, comprising portions of software code for carrying out the steps of the method according to claim 12 or 13.

Images