Search results for "diagnostiikka"
showing 10 items of 32 documents
Lack of association between screening interval and cancer stage in Lynch syndrome may be accounted for by over-diagnosis; a prospective Lynch syndrom…
2019
Background Recent epidemiological evidence shows that colorectal cancer (CRC) continues to occur in carriers of pathogenic mismatch repair (path_MMR) variants despite frequent colonoscopy surveillance in expert centres. This observation conflicts with the paradigm that removal of all visible polyps should prevent the vast majority of CRC in path_MMR carriers, provided the screening interval is sufficiently short and colonoscopic practice is optimal. Methods To inform the debate, we examined, in the Prospective Lynch Syndrome Database (PLSD), whether the time since last colonoscopy was associated with the pathological stage at which CRC was diagnosed during prospective surveillance. Path_MMR…
Molecular Basis of Mismatch Repair Protein Deficiency in Tumors from Lynch Suspected Cases with Negative Germline Test Results
2020
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The Value of Liquid Biopsies for Guiding Therapy Decisions in Non-small Cell Lung Cancer
2019
Targeted therapies have allowed for an individualized treatment approach in non-small-cell lung cancer (NSCLC). The initial therapeutic decisions and success of targeted therapy depend on genetic identification of personal tumor profiles. Tissue biopsy is the gold standard for molecular analysis, but non-invasive or minimally invasive liquid biopsy methods are also now used in clinical practice, allowing for later monitoring and optimization of the cancer treatment. The inclusion of liquid biopsy in the management of NSCLC provides strong evidence on early treatment response, which becomes a basis for determining disease progression and the need for changes in treatment. Liquid biopsies can…
Orderly display of limb lead ECGs raises Chinese intern’s diagnostic accuracy when determining frontal plane QRS axis
2019
Background: There is limited information on whether the orderly display of limb lead ECGs (electrocardiograms) can facilitate students to determine frontal plane QRS complex wave electrical axis. Objectives: The study investigated whether the orderly display of limb lead ECGs can raise Chinese undergraduate intern’s diagnostic accuracy when determining frontal plane axis. Design: A total of 147 fifth-year undergraduate interns aged between 21 and 25 years were randomly arranged into 2 groups: one group was given classically displayed ECGs of limb leads while the other group was given orderly displayed ECGs of limb leads. They were then taught to determine frontal plane axis with one of the …
Detection of developmental dyslexia with machine learning using eye movement data
2021
Dyslexia is a common neurocognitive learning disorder that can seriously hinder individuals’ aspirations if not detected and treated early. Instead of costly diagnostic assessment made by experts, in the near future dyslexia might be identified with ease by automated analysis of eye movements during reading provided by embedded eye tracking technology. However, the diagnostic machine learning methods need to be optimized first. Previous studies with machine learning have been quite successful in identifying dyslexic readers, however, using contrasting groups with large performance differences between diagnosed and good readers. A practical challenge is to identify also individuals with bord…
One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer
2020
Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment. In this research, the goal is to demonstrate the use of one-pixel attacks in a real-life scenario with a real pathology dataset, TUPAC16, which consists of digitized whole-slide images. We attack against the IBM CODAIT's MAX breast cancer detector using adversarial images. These adversarial examples are found using differential evolution to perform the one-pixel modification to the images in the dataset. The results indicate that a mino…
Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
2021
There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module.
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
2022
Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automatic analysis in the future. Especially, the recent success of various deep machine learning models shows promise that, in the future, patients could self-analyse their external signs of skin cancer by uploading pictures of these signs to an artificial intelligence system, which runs such a deep learning model and returns the classification results. However, both patients and dermatologists, who might use such a system to aid their work, need to …
Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions
2022
Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasi…