Jim Thomas

[YY Yan @yan_yet]

Register while seats last !! @JimTPDHallinan

[Benjamin Tan @DrBenjaminTan]

CHRISTMAS Study performed by our colleagues at the @NUHS_sg #MerryChristmas

[Marcelo Bordalo @MarceloBordalo]

Wonderful 3 days teaching at the @IDKDCourse in Singapore 🌞. Proud of being part of this incredible team and grateful to the warm hosts at @NUSingapore. 🙏🏼 @PekKhong @JanFritzMSK @HillaryGarnerMD @BredellaMD @jhodler @SutterBalgrist #mskradiology #medicaleducation

[Jim Thomas @JimTPDHallinan]

@dyannotdiane @mnpathria @Darwinrx @catalmeji Great work !

[RadioGraphics @RadioGraphics]

Access the “Top 10 Must Read” article list for each radiology subspecialty, curated specifically by radiology trainees (#RGTEAM) for radiology trainees. https://t.co/jyb8NOasA6

[Jim Thomas @JimTPDHallinan]

This is an editorial accompanying the following article: Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability https://t.co/scTDiPcxGL

[Jim Thomas @JimTPDHallinan]

Deep Learning for Spine MRI: Reducing Time Not Quality https://t.co/Ov7fAM96F8

[Salvador Pedraza @salvasapedraza]

Improved Productivity Using Deep Learning–assisted Reporting for Lumbar Spine MRI . Desmond Shi Wei Lim et al. https://t.co/hH08ymarc7

[Radiology @radiology_rsna]

Almansour et al developed a deep learning–based MRI turbo spin-echo (TSE) method that was interchangeable with standard TSE for detecting spine abnormalities while resulting in a 70% reduction in examination time. @HalmansourMD @AfatDr @MedTuebingen https://t.co/tFZnHcA0aW

[Curt Langlotz @curtlanglotz]

Hot off the press: Collaborative development of organizational best practices for #AI implementation, led by @DaniaDaye + @mattlungrenMD @walterfwiggins @talkasab @RadKottler @bibballen @ChrisRothMD @BernardoBizzo @JimBrinkMD @larson_david_b @stanfordAIMI https://t.co/SUcmow5cIh

[Reto Sutter @SutterBalgrist]

Our study is in the new issue of #Radiology by @radiology_rsna (Impact factor 29.1):
Re-assessing safety in #hip & #knee #injections: Much lower complication rate (1%) than reported in previous smaller studies (10-17%).
https://t.co/TDuS9ulKBh
@derbalgrist | @uzh_ch | #MSKrad

[Jim Thomas @JimTPDHallinan]

Deep learning for early detection of metastatic cord compression on staging CT. We need to help body radiologists pick up these lesions. Many thanks to all my coauthors including @AndrewMakmur, @DesmondLimSW

[Jim Thomas @JimTPDHallinan]

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT https://t.co/Fs5CMHUavc #mdpicancers via @Cancers_MDPI

[Radiology @radiology_rsna]

Eight radiologists who retrospectively reviewed lumbar spine MRI studies with the assistance of deep learning had a reduced interpretation time of lumbar spinal stenosis per study compared with unassisted radiologists. @JimTPDHallinan @AndrewMakmur https://t.co/CJTynW3TDx

[Radiology @radiology_rsna]

In this targeted editorial, Dr Hayashi of @StonyBrookMed discusses the need for multi-task deep learning tools for clinical practice and current applications. https://t.co/xZtpfNCuGa

[YY Yan @yan_yet]

Register for SGCR WIRES 2022 Pain workshop! Stellar international and locoregional speakers lined up. Webinar + hands on cadaver workshop #radiology #radiologyconference #interventionaloncology

[Jim Thomas @JimTPDHallinan]

Thank you to all our local and international AI collaborators for all the immense support and encouragement over the past few years!

[Jim Thomas @JimTPDHallinan]

Excited to share our paper in the latest issue of Radiology! https://t.co/pcy6mjzwaq

Lower back pain due to lumbar spinal stenosis is a major reason for seeking medical care and a major part of radiologists’ workload (very repetitive and monotonous).

[Jim Thomas @JimTPDHallinan]

Radiologists who were assisted by our deep learning model for the lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted.