Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns.

Significance: Glaucoma, a leading cause of global blindness, disproportionately affects low-income regions due to expensive diagnostic methods. Affordable intraocular pressure (IOP) measurement is crucial for early detection, especially in low- and middle-income countries. Aim: We developed a remote photonic IOP biomonitoring method by deep learning of the speckle patterns reflected from an eye sclera stimulated by a sound source. We aimed to achieve precise IOP measurements. Approach: IOP was artificially raised in 24 pig eyeballs, considered similar to human eyes, to apply our biomonitoring method. By deep learning of the speckle pattern videos, we analyzed the data for accurate IOP determination. Results: Our method demonstrated the possibility of high-precision IOP measurements. Deep learning effectively analyzed the speckle patterns, enabling accurate IOP determination, with the potential for global use. Conclusions: The novel, affordable, and accurate remote photonic IOP biomonitoring method for glaucoma diagnosis, tested on pig eyes, shows promising results. Leveraging deep learning and speckle pattern analysis, together with the development of a prototype for human eyes testing, could enhance diagnosis and management, particularly in resource-constrained settings worldwide.

Full-optical photoacoustic imaging using speckle analysis and resolution enhancement by orthogonal pump patterns projection.

This paper presents an approach for achieving full optical photoacoustic imaging with enhanced resolution utilizing speckle pattern analysis. The proposed technique involves projecting patterns derived from binary masks corresponding to orthogonal functions onto the target to elicit a photoacoustic signal. The resulting signal is then recorded using a high-speed camera and analyzed using correlation analysis of the speckle motion. Our results demonstrate the feasibility of this optical approach to achieve imaging with enhanced resolution without the need for physical contact with the target, opening up new possibilities for non-invasive medical imaging and other applications.

Optical Multimode Fiber-Based Pipe Leakage Sensor Using Speckle Pattern Analysis.

Water is an invaluable resource quickly becoming scarce in many parts of the world. Therefore, the importance of efficiency in water supply and distribution has greatly increased. Some of the main tools for limiting losses in supply and distribution networks are leakage sensors that enable real-time monitoring. With fiber optics recently becoming a commodity, along with the sound advances in computing power and its miniaturization, multipurpose sensors relying on these technologies have gradually become common. In this study, we explore the development and testing of a multimode optic-fiber-based pipe monitoring and leakage detector based on statistical and machine learning analyses of speckle patterns captured from the fiber's outlet by a defocused camera. The sensor was placed inside or over a PVC pipe with covered and exposed core configurations, while 2 to 8 mm diameter pipe leaks were simulated under varied water flow and pressure. We found an overall leak size determination accuracy of 75.8% for a 400 µm covered fiber and of 68.3% for a 400 µm exposed fiber and demonstrated that our sensor detected pipe bursts, outside interventions, and shocks. This result was consistent for the sensors fixed inside and outside the pipe with both covered and exposed fibers.

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

Significance: Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods. Aim: We aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection. Approach: The proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject's finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification. Results: The study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient. Conclusions: The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

Optical sensor for remote estimation of CO2 concentration in the blood stream.

Speckle pattern analysis become a widespread method for remote sensing of various biomedical parameters. This technique is based on tracking the secondary speckle patterns reflected from a human skin illuminated by a laser beam. Speckle pattern variations can be translated into the corresponding partial carbon dioxide (CO2) state (High or Normal) in the bloodstream. We present a novel approach for remote sensing of human blood carbon dioxide partial pressure (PCO2) based on speckle pattern analyses combined with machine learning approach. The blood CO2 partial pressure is an important indicative parameter for a variety of malfunctions in the human body.

Analysis of swallowing in infants and adults using speckle pattern analysis.

The ability to detect and evaluate ingestion is especially important in toddlers. The development of new methods for detecting and accurately measuring ingestion is therefore extremely important. One of the methods allowing such measurements is speckle pattern analyses, a well-known phenomenon in coherent imaging. The method allows extraction of various medical parameters, such as blood pulse pressure, temporal signature of heartbeats and breath. The current work contains further development and application of the speckle tracking technique for remote detection and quantification of swallowing and distinguishing between sucking and swallowing to identify feeding disorders in infants and elderly individuals.

Remote photonic detection of human senses using secondary speckle patterns.

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano-vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self-interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns.

Remote photonic sensing of blood oxygen saturation via tracking of anomalies in micro-saccades patterns.

Speckle pattern analysis has been found by many researchers to be applicable to remote sensing of various biomedical parameters. This paper shows how analysis of dynamic differential speckle patterns scattered from subjects' sclera illuminated by a laser beam allows extraction of micro-saccades movement in the human eye. Analysis of micro-saccades movement using advanced machine learning techniques based on convolutional neural networks offers a novel approach for non-contact assessment of human blood oxygen saturation level (SpO2). Early stages of hypoxia can rapidly progress into pneumonia and death, and lives can be saved by advance remote detection of reduced blood oxygen saturation.

Monitoring of vital bio-signs by analysis of speckle patterns in a fabric-integrated multimode optical fiber sensor.

Continuous noninvasive measurement of vital bio-signs, such as cardiovascular parameters, is an important tool in evaluation of the patient's physiological condition and health monitoring. Based on new enabling technologies, continuous monitoring of heart and respiration rate, pulse wave velocity and blood pressure have been investigated, advanced and reflected in numerous papers published in recent years. In this paper, we introduce a new technique for noninvasive sensing of vital bio-signs based on a multimode optical fiber sensor that can be integrated into a fabric. The sensor consists of a laser, optical fiber, video camera and computer. Its operation is based on tracking of point-wise intensity variations on speckle patterns caused by interference of the light modes within the fiber subjected to deformation. The paper contains theoretical analysis and experimental validation of the proposed scheme. The main goal is to advance a simple low-cost sensor embedded in a cloth fabric to track changes in the cardiovascular condition of the wearer.

Intraocular pressure remote photonic biomonitoring based on temporally encoded external sound wave stimulation.

Continuous noninvasive measurement of intraocular pressure (IOP) is an important tool in the evaluation process for glaucoma. We present a methodology enabling high-precision, noncontact, reproducible, and continuous monitoring of IOP based on the value of the damping factor of transitional oscillations obtained at the surface of the eye after terminating its stimulation by a sound wave. The proposed configuration includes projection of a laser beam and usage of a fast camera for analyzing the temporal-spatial variations of the speckle patterns backscattered from the iris or the sclera following the above-mentioned sound waves external stimulation. The methodology was tested on an artificial eye and a carp fish eye under varying pressure as well as on human eyes.

Approach to breast cancer early detection via tracking of secondary speckle patterns reflected from the skin with artificial intradermal impurity.

Breast cancer has become a major cause of death among women. The lifetime risk of a woman developing this disease has been established as one in eight. The most useful way to reduce breast cancer death is to treat the disease as early as possible. The existing methods of early diagnostics of breast cancer are mainly based on screening mammography or Magnetic Resonance Imaging (MRI) periodically conducted at medical facilities. In this paper the authors proposing a new approach for simple breast cancer detection. It is based on skin stimulation by sound waves, illuminating it by laser beam and tracking the reflected secondary speckle patterns. As first approach, plastic balls of different sizes were placed under the skin of chicken breast and detected by the proposed method.

Monitoring blood vital bio signs using secondary speckle patterns.

Continuous noninvasive measurement of vital bio-signs, such as cardiopulmonary parameters, is an important tool in the evaluation process of the patient's physiological condition and in the health monitoring of the patient. On the demand of new enabling technologies, some works have been done in continuous monitoring of blood pressure and pulse wave velocity. In this paper, we introduce further application of a novel technique for remote noncontact blood pulse wave velocity and pressure measurement based on tracking the temporal changes of reflected secondary speckle patterns produced in human skin when illuminated by a laser beam. The main goal of this work is to offer novel, simple and low cost measurement tool which will indicate changes in condition of the cardiovascular system after having an initial calibration.

Monitoring blood vital bio signs using secondary speckle patterns.

Continuous noninvasive measurement of vital bio-signs, such as cardiopulmonary parameters, is an important tool in the evaluation process of the patient's physiological condition and in the health monitoring of the patient. On the demand of new enabling technologies, some works have been done in continuous monitoring of blood pressure and pulse wave velocity. In this paper, we introduce further application of a novel technique for remote noncontact blood pulse wave velocity and pressure measurement based on tracking the temporal changes of reflected secondary speckle patterns produced in human skin when illuminated by a laser beam. The main goal of this work is to offer novel, simple and low cost measurement tool which will indicate changes in condition of the cardiovascular system after having an initial calibration.