Hearing is one of the most crucial senses for all humans.It allows people to hear and connect with the environment, the people they can meet and the knowledge they need to live their lives to the fullest.Hearing loss can have a detrimental impact on a person's quality of life in a variety of ways, ranging from fewer educational and job opportunities due to impaired communication to social withdrawal in severe situations.Early diagnosis and treatment can prevent most hearing loss.Pure tone audiometry, which measures air and bone conduction hearing thresholds at various frequencies, is widely used to assess hearing loss.A shortage of audiologists might delay diagnosis since they must analyze an audiogram, a graphic representation of pure tone audiometry test results, to determine hearing loss type and treatment.In the presented work, several AI-based models were used to classify audiograms into three types of hearing loss: mixed, conductive, and sensorineural.These models included Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, Decision Trees, RandomForest, Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Graph Neural Network (GNN), and Recurrent Neural Network (RNN).The models were trained using 4007 audiograms classified by experienced audiologists.The RNN architecture achieved the best classification performance, with an out-of-training accuracy of 94.46%.Further research will focus on increasing the dataset and enhancing the accuracy of RNN models.

In this study, we present a comparison of popular methods for the interpolation of irregular spatial data in order to determine the applicability of each algorithm for hyperspectral reflectance estimation. The algorithms were benchmarked against a very high-resolution orthoimage from an RGB camera and medium-resolution satellite imagery from Sentinel-2A. We tested five interpolation algorithms: Triangulated Irregular Network (TIN), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Universal Kriging (UK), and Kriging with external drift (KED) for transformation of spectrometer reflectance footprint into reflectance images. Next we tested four downsampling methods: Box filtering, Gaussian, Catmull-Rom (Catrom) and Lanczos to compare the interpolated reflectance images with Sentinel-2 reflectance. Overall the best interpolation was achieved with the KED algorithm, and the lowest errors were achieved with the Gaussian downsampling method.

Hearing loss is one of the most significant sensory disabilities.It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases.It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately.Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing threshold both in air and bone conduction at different frequencies.The graphic result of this test is represented on an audiogram, which is a diagram depicting the values of the patient's measured hearing thresholds.In the course of the presented work several different artificial neural network models, including MLP, CNN and RNN, have been developed and tested for classification of audiograms into two classes -normal and pathological represented hearing loss.The networks have been trained on a set of 2400 audiograms analysed and classified by professional audiologists.The best classification performance was achieved by the RNN architecture (represented by simple RNN, GRU and LSTM), with the highest out-of-training accuracy being 98% for LSTM.In clinical application, the developed classifier can significantly reduce the workload of audiology specialists by enabling the transfer of tasks related to analysis of hearing test results towards general practitioners.The proposed solution should also noticeably reduce the patient's average wait time between taking the hearing test and receiving a diagnosis.Further work will concentrate on automating the process of audiogram interpretation for the purpose of diagnosing different types of hearing loss.

Expert systems for diagnosis of tropical diseases have been developed and implemented for over a decade with varying degrees of success. While the recent introduction of artificial neural networks has helped to improve the diagnosis accuracy of such systems, this aspect is still negatively affected by the number of supported diseases. A large number of supported diseases usually corresponds to a high number of overlapping symptoms, which results in a considerable drop in diagnosis accuracy. This is particularly important when diagnosing patients returning from holiday or business trips which took them through a number of different tropical regions. The paper presents a system dedicated to diagnosis of patients returning from various tropical countries. The system integrates an artificial neural network with a Geographic Information System (GIS) in order to enhance the diagnostic process. As a result, the system provides several layers of diagnosis support, depending on the types and number of provided patient characteristics. The system has been developed in cooperation with the University Center of Maritime and Tropical Medicine located in the city of Gdynia in northern Poland and applied to diagnosis of patients with malaria, dengue and stechiostomasis with promising results.