LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 4 of total 4

Search options

  1. Article: Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis.

    Mitchell, Sian / Nikolopoulos, Manolis / El-Zarka, Alaa / Al-Karawi, Dhurgham / Al-Zaidi, Shakir / Ghai, Avi / Gaughran, Jonathan E / Sayasneh, Ahmad

    Cancers

    2024  Volume 16, Issue 2

    Abstract: Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. ... ...

    Abstract Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.
    Language English
    Publishing date 2024-01-19
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers16020422
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients

    Al-karawi, Dhurgham / Al-Zaidi, Shakir / Polus, Nisreen / Jassim, Sabah

    medRxiv

    Abstract: This paper reports on the development and performance of machine learning schemes for the analysis of Chest CT Scan images of Coronavirus COVID-19 patients and demonstrates significant success in efficiently and automatically testing for COVID-19 ... ...

    Abstract This paper reports on the development and performance of machine learning schemes for the analysis of Chest CT Scan images of Coronavirus COVID-19 patients and demonstrates significant success in efficiently and automatically testing for COVID-19 infection. In particular, an innovative frequency domain algorithm, to be called FFT-Gabor scheme, will be shown to predict in almost real-time the state of the patient with an average accuracy of 95.37%, sensitivity 95.99% and specificity 94.76%. The FFT-Gabor scheme is adequately informative in that clinicians can visually examine the FFT-Gabor feature to support their final diagnostic.
    Keywords covid19
    Language English
    Publishing date 2020-04-17
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.04.13.20063479
    Database COVID19

    Full text online

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients

    Al-karawi, Dhurgham / Al-Zaidi, Shakir / Polus, Nisreen / Jassim, Sabah

    medRxiv

    Abstract: Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of ... ...

    Abstract Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of the pandemic to all continents, albeit with a nonuniform community transmission, necessitates chest imaging for medical triage of patients presenting moderate-severe clinical COVID-19 features. This paper reports the development of innovative machine learning schemes for the analysis of Chest X-Ray (CXR) scan images of COVID-19 patients in almost real-time, demonstrating significantly high accuracy in identifying COVID-19 infection. The performance testing was conducted on a combined dataset comprising CXRs of positive COVID-19 patients, patients with various viral and bacterial infections, as well as persons with a clear chest. The test resulted in successfully distinguishing CXR COVID-19 infection from the other cases with an average accuracy of 94.43%, sensitivity 95% and specificity 93.86%.
    Keywords covid19
    Language English
    Publishing date 2020-05-08
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.05.05.20091561
    Database COVID19

    Full text online

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article ; Online: Reduced translucency and the addition of black patterns increase the catch of the greenhouse whitefly, Trialeurodes vaporariorum, on yellow sticky traps.

    Sampson, Clare / Covaci, Anca D / Hamilton, James G C / Hassan, Nayem / Al-Zaidi, Shakir / Kirk, William D J

    PloS one

    2018  Volume 13, Issue 2, Page(s) e0193064

    Abstract: The greenhouse whitefly Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) is a pest of a wide range of vegetable and ornamental crops in greenhouses around the world. Yellow sticky traps are highly attractive to flying adults and so are ... ...

    Abstract The greenhouse whitefly Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) is a pest of a wide range of vegetable and ornamental crops in greenhouses around the world. Yellow sticky traps are highly attractive to flying adults and so are frequently used to monitor the pest. Our aim was to test whether changes in trap translucency or the addition of printed black patterns could increase the catch on yellow sticky traps in greenhouses. Field trials were carried out in commercial crops of strawberry and tomato under glass over three years. Reduced trap translucency increased trap catches by a factor of 1.5 to 7.0 and the catch increased significantly for both females and males. Spectrometer measurements showed that the increased catch was consistent with an increased landing stimulus from a colour opponency mechanism i.e. the ratio of energy from 500-640 nm to the energy from 300-500 nm. Printed black patterns increased trap catches on specific types of trap, by a factor of 1.4 to 2.3, and the catch increased significantly for both females and males. The patterns increased trap catch on moderately translucent traps, but decreased trap catch on less translucent traps. The evidence points to a contrast/edge effect of pattern, but laboratory experiments are needed to clarify this. Exploitation of these translucency and pattern effects could improve the efficacy of yellow traps for monitoring and mass trapping in crops.
    MeSH term(s) Animals ; Crop Production/methods ; Female ; Hemiptera ; Insect Control/instrumentation ; Insect Control/methods ; Male
    Language English
    Publishing date 2018
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0193064
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top