• Klíčová slova
• diagnostika, zobrazovací metody, zobrazovací diagnostika, počítačová diagnostika,

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• diagnostika
• lékařská informatika

• MeSH
• automatizace MeSH
• diagnóza počítačová MeSH
• lidé MeSH
• poradenství pro volbu povolání MeSH
• psychologické testy MeSH
• psychologie práce metody   přístrojové vybavení   MeSH
• Check Tag
• lidé MeSH

• MeSH
• diagnóza počítačová MeSH
• počítačová rentgenová tomografie MeSH
• ultrasonografie MeSH
• Publikační typ
• kongresy MeSH
• sborníky MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• radiologie, nukleární medicína a zobrazovací metody

• MeSH
• diagnóza počítačová metody   trendy   MeSH
• Publikační typ
• sborníky MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• radiologie, nukleární medicína a zobrazovací metody
• radiologie, nukleární medicína a zobrazovací metody

• MeSH
• diagnóza počítačová MeSH
• hemodynamika MeSH
• koronární nemoc MeSH
• myokard patologie   MeSH
• srdeční komory MeSH

• Publikační typ
• abstrakty MeSH

• MeSH
• automatizované zpracování dat MeSH
• diagnóza počítačová metody   přístrojové vybavení   MeSH
• gastrointestinální nemoci diagnóza   klasifikace   MeSH
• počítače využití   MeSH
• terminologie jako téma MeSH

• MeSH
• chronické poškození mozku diagnóza   MeSH
• diagnóza počítačová MeSH
• duševní poruchy MeSH
• lidé MeSH
• Check Tag
• lidé MeSH

BACKGROUND AND PURPOSE: MRA is widely accepted as a noninvasive diagnostic tool for the detection of intracranial aneurysms, but detection is still a challenging task with rather low detection rates. Our aim was to examine the performance of a computer-aided diagnosis algorithm for detecting intracranial aneurysms on MRA in a clinical setting. MATERIALS AND METHODS: Aneurysm detectability was evaluated retrospectively in 48 subjects with and without computer-aided diagnosis by 6 readers using a clinical 3D viewing system. Aneurysms ranged from 1.1 to 6.0 mm (mean = 3.12 mm, median = 2.50 mm). We conducted a multireader, multicase, double-crossover design, free-response, observer-performance study on sets of images from different MRA scanners by using DSA as the reference standard. Jackknife alternative free-response operating characteristic curve analysis with the figure of merit was used. RESULTS: For all readers combined, the mean figure of merit improved from 0.655 to 0.759, indicating a change in the figure of merit attributable to computer-aided diagnosis of 0.10 (95% CI, 0.03-0.18), which was statistically significant (F(1,47) = 7.00, P = .011). Five of the 6 radiologists had improved performance with computer-aided diagnosis, primarily due to increased sensitivity. CONCLUSIONS: In conditions similar to clinical practice, using computer-aided diagnosis significantly improved radiologists' detection of intracranial DSA-confirmed aneurysms of ≤6 mm.

• MeSH
• algoritmy * MeSH
• diagnóza počítačová metody   MeSH
• intrakraniální aneurysma radiografie   MeSH
• lidé MeSH
• magnetická rezonanční angiografie metody   MeSH
• retrospektivní studie MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.

• MeSH
• biopsie MeSH
• hyperplazie MeSH
• lidé MeSH
• počítače * MeSH
• reprodukovatelnost výsledků MeSH
• studie proveditelnosti MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH