Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

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Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions as good as humans (or even better). AI is already a part of our daily life – it is behind face recognition, speech recognition in virtual assistants (like Amazon Alexa, Apple's Siri, Google Assistant, and Microsoft...

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Xehetasun bibliografikoak
Egile Nagusiak:	Drukker, L, Noble, JA, Papageorghiou, AT
Formatua:	Journal article
Hizkuntza:	English
Argitaratua:	Wiley 2020

Antzeko izenburuak

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology
nork: Drukker, L, et al.
Argitaratua: (2021)

Crc handbook of ultrasound in obstetrics and gynecology /
nork: Kurjak, Asim
Argitaratua: (1990)

Automatic probe movement guidance for freehand obstetric ultrasound
nork: Droste, R, et al.
Argitaratua: (2020)

Basic gynecology and obstetrics
nork: Cunningham, F Gary, et al.
Argitaratua: (1993)

Safety indices of ultrasound: Adherence to recommendations and awareness during routine obstetric ultrasound scanning
nork: Drukker, L, et al.
Argitaratua: (2020)

Function and safety of SlowflowHD ultrasound doppler in obstetrics
nork: Drukker, L, et al.
Argitaratua: (2022)

Gaze-probe joint guidance with multi-task learning in obstetric ultrasound scanning
nork: Men, Q, et al.
Argitaratua: (2023)

Multimodal-GuideNet: gaze-probe bidirectional guidance in obstetric ultrasound scanning
nork: Men, Q, et al.
Argitaratua: (2022)

Benson & Pernoll's handbook of obstetrics & gynecology /
nork: 454582 Pernoll, Martin L.
Argitaratua: (2001)

Sonography in gynecology and obstetrics : just the facts /
nork: Fleischer, Arthur C.
Argitaratua: (2004)

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
nork: Drukker, L, et al.
Argitaratua: (2021)

International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) - the propagation of knowledge in ultrasound for the improvement of OB/GYN care worldwide: experience of basic ultrasound training in Oman
nork: Vrachnis, N, et al.
Argitaratua: (2019)

OC10.02: Bioeffects safety indices of ultrasound: quantifying adherence to recommendations on routine obstetric scan
nork: Drukker, L, et al.
Argitaratua: (2019)

Captioning ultrasound images automatically
nork: Alsharid, M, et al.
Argitaratua: (2019)

Step by step ultrasound in gynecology /
nork: 421259 Singh, Kuldeep, et al.
Argitaratua: (2004)

22nd World Congress on Ultrasound in Obstetrics and Gynecology, 9-13 September 2012, Copenhagen, Denmark: presentations and awards
nork: Kohl, T, et al.
Argitaratua: (2013)

Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging
nork: Sharma, H, et al.
Argitaratua: (2021)

Tackling poorly selected, collected, and reported outcomes in obstetrics and gynecology research
nork: Duffy, J, et al.
Argitaratua: (2018)

Sistem diagnosis dan rawatan penyakit dalam obstetric & gynecologic (SDRPOG) /
nork: 369659 Teo, Hui Sin
Argitaratua: (2003)

Developing clinical artificial intelligence for obstetric ultrasound to improve access in underserved regions: protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) study
nork: Self, A, et al.
Argitaratua: (2022)

Imaging in gynecological disease: clinical and ultrasound features of immature teratomas of the ovary
nork: Landolfo, C, et al.
Argitaratua: (2016)

Ultrasound in obstetrics and gynaecology /
nork: Wladimiroff, Juriy W., et al.
Argitaratua: (2009)

A curriculum learning based approach to captioning ultrasound images
nork: Alsharid, M, et al.
Argitaratua: (2020)

Weakly supervised captioning of ultrasound images
nork: Alsharid, M, et al.
Argitaratua: (2022)

Efficient ultrasound image analysis models with sonographer gaze assisted distillation
nork: Patra, A, et al.
Argitaratua: (2018)

Assessment of obstetric ultrasound images using machine learning
nork: Rahmatullah, B
Argitaratua: (2012)

Multi-modal learning from video, eye tracking, and pupillometry for operator skill characterization in clinical fetal ultrasound
nork: Sharma, H, et al.
Argitaratua: (2021)

Self-knowledge distillation for first trimester ultrasound saliency prediction
nork: Gridach, M, et al.
Argitaratua: (2022)

Step by step ultrasound in obstetrics /
nork: 421259 Singh, Kuldeep, et al.
Argitaratua: (2004)

Anatomy-aware contrastive representation learning for fetal ultrasound
nork: Fu, Z, et al.
Argitaratua: (2023)

Towards scale and position invariant task classification using normalised visual scanpaths in clinical fetal ultrasound
nork: Teng, C, et al.
Argitaratua: (2021)

Self-supervised contrastive video-speech representation learning for ultrasound
nork: Jiao, J, et al.
Argitaratua: (2020)

Ultrasound image representation learning by modeling sonographer visual attention
nork: Droste, R, et al.
Argitaratua: (2019)

The gynecological sourcebook /
nork: 369762 Rosenthal, M. Sara
Argitaratua: (2003)

Developmental origins of health and disease: knowledge, attitude and practice of obstetrics & gynecology residents, pediatric residents, and medical students
nork: Ku, Chee Wai, et al.
Argitaratua: (2023)

The implementation of the balanced scorecard in achieving the fast response maternity hospital at the special hospital for obstetrics and gynecology of Bhakti Ibu
nork: , MAYASARI, Iin, et al.
Argitaratua: (1998)

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
nork: Bowness, JS, et al.
Argitaratua: (2022)

Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia.
nork: Bowness, JS, et al.
Argitaratua: (2022)

D2ANET: Densely Attentional-Aware Network for first trimester ultrasound CRL and NT segmentation
nork: Gridach, M, et al.
Argitaratua: (2023)

Self-supervised ultrasound to MRI fetal brain image synthesis
nork: Jiao, J, et al.
Argitaratua: (2020)