Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.

Optical coherence tomography (OCT) has become the best imaging tool to assess calcified plaque and nodule. However, every OCT pullback has numerous images, and artificial analysis requires too much time and energy. Thus, it is unsuitable for clinical application. This study aimed to develop and validate an automatic assessment of calcified plaque and nodule by OCT using deep-learning model. The OCT images of calcified plaque and nodule were labeled by two expert readers based on the consensus. A deep-learning model with a MultiScale and MultiTask u-net network (MS-MT u-net) was developed. Then, with the ground truth labeled by expert readers as reference, the diagnostic accuracy and agreement of the model was validated. For the pixelwise evaluation of calcified plaque, the model had a high performance with precision (93.95%), recall (88.95%), and F1 score (91.38%). For the lesion-level evaluation of calcified plaque, the quantitative metrics by the model excellently correlated with the ground truth (calcium score, r = 0.90, p < 0.01; calcified volume, r = 0.99, p < 0.01). For calcified nodules, the model showed excellent diagnostic performance including sensitivity (91.7%), specificity (89.3%), and accuracy (91.0%). We developed a novel deep-learning model to identify the attributes of calcified plaque and nodule. This model provided excellent diagnostic accuracy and agreement with the ground truth, thereby reducing the subjectivity of manual measurements and substantially saving time. These findings can help practitioners efficiently adopt appropriate therapeutic strategies to treat calcified lesions.

Disease burden and related medical costs of rotavirus infections in Taiwan.

BACKGROUND: The disease burden and associated medical costs of rotavirus infections in inpatient and outpatient sectors in Taiwan were examined in anticipation of the availability of new rotavirus vaccines. METHODS: The yearly national case number and medical costs for all for inpatients and outpatients with acute gastroenteritis (AGE) were extracted from the Bureau of National Health Insurance database in Taiwan according to ICD-9-CM codes. A retrospective study was also performed using records of children with AGE seen at three hospitals in Taiwan in 2001 to identify laboratory confirmed rotavirus infection cases. The annual incidence and related medical costs of AGE due to rotavirus infection were then estimated. RESULTS: Children <5 years old comprised 83.6% of inpatient and 62.0% of outpatient pediatric AGE cases in Taiwan in 2001. Rotavirus was the most common agent detected among AGE patients in this age group in the three hospitals, and was detected in 32.9% (221/672) of inpatient and 24% (23/96) of outpatient stool specimens tested for microbial etiologies. An estimated 277,400 to 624,892 cases of rotavirus infections sought medical care in Taiwan in 2001, equaling one in 2 to 5 children <5 years old required medical care due to rotavirus infection. The incidence of hospitalization due to rotavirus infections was 1,528-1,997/100,000 for children <5 years old. The total associated medical costs due to rotavirus infection were estimated at US $10-16 millions in Taiwan in 2001. Although the per-capita medical cost of rotavirus infection was lower in Taiwan than in the United States or Hong Kong, the personal economic burden was similar among the three places when normalized for gross national incomes per capita. CONCLUSION: Infections caused by rotavirus constitute an important human and economic burden among young children in Taiwan. A safe and effective vaccine is urgently needed.