The Automated Expert Support System for Optic Nerve Head Morphological Description

Purpose. To develop the automated expert support system for optic nerve head morphological description in normal conditions and in pathology.Methods. The proposed expert support system is based on the integration algorithm of luminance samples along the diagonal, it allows to detect the optic nerve head border. On the basis of this algorithm the method for solving the following tasks of fundus image processing have been proposed: detecting of the optic nerve head border, method of the morphological description of the optic nerve head boundary, method of the determining the value of the disk excavation. An experimental study of the parameters effect on the effectiveness of the optic nerve head detecting method was made.Results. The effectiveness assessment of the proposed border detection algorithm on the optic nerve head model has showed that the amount of overlap averaged 0.985, which indicates high quality. It was found that the algorithm for estimating the diameter of the single-sided optic nerve head image is sufficiently resistant to changes in such parameters as the influence of the noise level in the scene and the offset of the strobe center coordinates of the samples accumulation from the image center coordinates. Evaluation of the efficiency of the optic nerve head borders morphological description has showed that the value of the first-order derivative of the result of accumulation of luminance readings diagonally for images of optic nerve head with blurred boundaries is 2 times smaller than for images of optic nerve head with clear boundaries. The effectiveness of the method of selecting the border for assessment the disk excavation size was examined. It was obtained that the error in estimating the magnitude of excavation amounted to an average of 8.43 %.Conclusions. Тhe presented expert support system allows to automate the process of optic disk morphological description, in particular, such parameters as the state of the border and the size of the disc excavation. This method can be used to create medical expert systems and software for fundus images processing.

Diagnosis Prediction of Tumours of Unknown Origin Using Immunogenius, A Machine Learning-based Expert Support System for Immunohistochemistry Profile Interpretation

Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions. MethodsWe developed ImmunoGenius, a machine-learning-based expert support system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on the Bayesian theorem, we developed the reactive native mobile application for iOS and Android platform. ImmunoGenius include the IHC profile of 584 antibodies in 2009 neoplasms. ResultsWe trained the software using 634 real case data, validated it with 382 case data, and compared the precision hit rate. Precision hit rate of the training dataset was 78.5 % and the hit rate of the validation dataset was 78.0%, which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.ConclusionThe results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

Expert support system for occupational therapist in the identification of sensory profile

Introduction Based on the increasing usability of technology in healthcare, this paper discusses the use of an expert system (ES) to identify the sensory profile of patients starting Occupational Therapy, allowing the professional to make assertive decisions in establishing priorities in the therapeutic plan.Objective To develop a decision support system from the Infant/Toddler Sensory Profile.Method Structuring of an ES based on Infant/Toddler Sensory Profile, from terms translation into Portuguese, identification of variables and domain values involved, and construction of production rules.Results Twelve variables were registered for the construction of the ES, 6 of these were treated as goal-variables, 20 rules being built.Conclusion This ES is an important support to the occupational therapist in the decision-making process of treatment plans, determining priorities and respecting the sensory profile of each child. In addition, it must be noted that there is no equivalent system.