Matthew A. Michela

PACS/VNA
Why interoperability is suddenly a very real thing
Life Image President and CEO Matthew Michela explains why healthcare IT companies must step up now to enable interoperability and digital image sharing. As the healthcare delivery system grapples with a global pandemic, the consequences of noninteroperability are very real.
May 4, 2020
2020 05 04 23 22 1021 Michela Matthew
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SIIM 2026
SIIM: LLMs have tradeoffs in energy use for imaging tasks
Research presented at SIIM 2026 shows that reasoning LLMs have energy tradeoffs for their performances.
C-index for Cox regression and random survival forests models using two feature sets. Adding historical registry data decreases the C-index for Cox regression but increased it for the random survival forests model, consistent with the non-linear structure in the registry data.
MRI
AI gives a boost to cardiac MRI risk prediction
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AI
Philips Future Health Index quantifies benefits of AI in healthcare
AI-acquired standardized planes. Example ultrasound images acquired in clinical settings with the on-cart, AI-based software, illustrating automated acquisition and anatomic annotation of three standard planes: (A) S5 dorsal longitudinal plane of the wrist joint, (B) S11 transverse section of the proximal carpal tunnel, and (C) S14 transverse section of the second and third flexor tendons. A1 = trochlea A1, Cap = capitate, C-M.JS = recess of the capitate-metacarpal joint, D&Ss = flexor digitorum profundus and superficialis tendons, FCR = flexor carpi radialis tendon, FD = flexor digitorum tendon, FPL = flexor pollicis longus tendon, L-C.JS = recess of the lunate-capitate joint, Lun = lunate, MN = median nerve, Pis = pisiform, Rad = radius, R-C.JS = recess of the radiocarpal joint, Sca = scaphoid, UA = ulnar artery, UN = ulnar nerve, 3rd Met = third metacarpal.
Ultrasound
MSK AI tool for ultrasound shows promise for assisting novice imagers
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SIIM: LLMs have tradeoffs in energy use for imaging tasks
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Accurate risk prediction for coronary heart disease helps clinicians guide patient therapy and follow-up.
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C-index for Cox regression and random survival forests models using two feature sets. Adding historical registry data decreases the C-index for Cox regression but increased it for the random survival forests model, consistent with the non-linear structure in the registry data.
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2026 report takes AI beyond potential.
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MSK AI tool for ultrasound shows promise for assisting novice imagers
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The software evaluated for standard plane recognition and automatic key anatomy segmentation.
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AI-acquired standardized planes. Example ultrasound images acquired in clinical settings with the on-cart, AI-based software, illustrating automated acquisition and anatomic annotation of three standard planes: (A) S5 dorsal longitudinal plane of the wrist joint, (B) S11 transverse section of the proximal carpal tunnel, and (C) S14 transverse section of the second and third flexor tendons. A1 = trochlea A1, Cap = capitate, C-M.JS = recess of the capitate-metacarpal joint, D&Ss = flexor digitorum profundus and superficialis tendons, FCR = flexor carpi radialis tendon, FD = flexor digitorum tendon, FPL = flexor pollicis longus tendon, L-C.JS = recess of the lunate-capitate joint, Lun = lunate, MN = median nerve, Pis = pisiform, Rad = radius, R-C.JS = recess of the radiocarpal joint, Sca = scaphoid, UA = ulnar artery, UN = ulnar nerve, 3rd Met = third metacarpal.
fMRI reveals postmenopausal brain connectivity shifts in midlife women
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Network-level analysis comparing functional association strength between ROIs of pre- and postmenopausal groups. Lower levels of connectivity were identified among one cluster (F3,106 = 8.84, Puncorrected < 0.001, PFDR = 0.012) in the postmenopausal group, which included the right supramarginal gyrus anterior division, right planum temporale, and right Heschl’s gyrus, among others. FDR, false discovery rate; ROI, region of interest.
Air pollution tied to worse coronary disease on CT, especially in women
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Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).
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AI may provide early breast cancer alerts from mammograms
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Commercial AI systems could provide early alerts for breast cancer by picking out early signs of the disease on mammograms.
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Screening mammograms and AI score changes are depicted over time in two individuals with screen-detected cancer. Full-field digital mammograms show craniocaudal (top) and mediolateral (bottom) views of the left and right breast. The description below each mammogram indicates whether AI scores from three AI-based computer-aided detection systems (AI-1, AI-2, and AI-3) were above or below the 90th percentile. (A) Mammograms from three screening time points (in 2009, 2011, and 2014) in an individual who was diagnosed in February 2014 (at age 73 years) with a left-sided 25-mm grade 2 invasive breast carcinoma, no special type (arrow). Mammographic breast density was classified as BI-RADS B on all three mammograms. (B) Mammograms from four screening time points (in 2009, 2011, 2013, and 2015) in an individual who was diagnosed in October 2015 (at age 65 years) with a left-sided 45-mm grade 2 lobular carcinoma (arrow). Mammographic breast density was classified as BI-RADS C on all four mammograms. Screen-detected cancers were defined as those diagnosed within 90 days for individuals who were recalled at screening (*).
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