The Relationship between Bone Remodeling and the Clockwise Rotation of the Facial Skeleton: A Computed Tomographic Imaging-Based Evaluation

Background: Information on the onset and gender differences of midfacial skeletal changes, including the complete understanding of the theory behind the clockwise rotational theory, remains elusive. Methods: One hundred fifty-seven Caucasian individuals (10 men and 10 women aged 20 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 to 69 years, 70 to 79 years, and 80 to 89 years, and eight men and nine women aged 90 to 98 years) were investigated. Multiplanar computed tomographic scans with standardized angle and distance measurements in all three anatomical axes and in alignment to the sella-nasion (horizontal) line were conducted. Results: Both men and women displayed an increase in orbital floor angle (p < 0.001, maximum at 60 to 69 years), decrease in maxillary angle (p = 0.035, 40 to 49 years), increase in palate angle (p < 0.001, 50 to 59 years), increase in vomer angle (p = 0.022, 30 to 39 years), but a decrease in the pterygoid angle (p = 0.002, 80 to 89 years). Orbital width decreased (p < 0.001, 60 to 69 years), pyriform aperture width increased (p = 0.015, 60 to 69 years), and midfacial height decreased with aging (p < 0.001, 60 to 69 years). Conclusions: Age-related changes of the midfacial skeleton occurred independently of gender, but at various time points in different locations. The observed changes seem to be driven by a bone resorption center located in the posterior maxilla, rather than by a rotational movement of the facial skeleton

Volumetry versus linear diameter lung nodule measurement: an ultra-low-dose computed tomography lung cancer screening study

BACKGROUND: The DutchBelgian Randomized Lung Cancer Screening Trial (NELSON) used a volume-based protocol and significantly reduced the prevalence of false-positive results (2.1%). AIM: To compare the performance of manual linear diameter and semi-automated volumetric nodule measurement in the pilot project Moscow Lung Cancer Screening ultra-low-dose computed tomography pilot study. MATERIALS AND METHODS: The study included individuals with a lung nodule of at least 4 mm on baseline-computed tomography of the Moscow lung cancer screening between February 2017 and February 2018, without verified lung cancer diagnosis until 2020. The radiation dose was selected individually and did not exceed 1 mSv. All scans were assessed by three blinded readers to measure the maximum and minimum transversal nodule diameter and extrapolated volume. As a reference value of size and volume, the average value from the results of expert measurements was obtained. A false-positive nodule was defined as a nodule 6 mm/100 mm3 and a false-negative nodule as a nodule 6 mm/100 mm3. RESULTS: Overall, 293 patients were included (166 men; mean age, 64.6 5.3years); 199 lung nodules were 6 mm/100 mm3 and 94 were 6 mm/100 mm3. Regarding volumetric measurements, 32 [10.9%; 4 false-positive, 28 false-negative], 29 [9.9%; 17 false-positive, 12 false-negative], and 30 [10.2%; 6 false-positive, 24 false-negative] nodule discrepancies were reported by readers 1, 2, and 3 respectively. For linear diameter measurement, 92 [65.5%; 107 false-positive, 85 false-negative], 146 [49.8%; 58 false-positive, 88 false-negative], and 102 [34.8%; 23 false-positive, 79 false-negative] nodule discrepancies were reported by readers 1, 2, and 3 respectively. CONCLUSIONS: The use of lung nodule volumetry strongly reduces the number of false-positive and false-negative nodules compared with nodule diameter measurements, in an ultra-low-dose computed tomography lung cancer screening program

Analysis of the diagnostic and economic impact of the combined artificial intelligence algorithm for analysis of 10 pathological findings on chest computed tomography

BACKGROUND: Artificial intelligence technology can help solve the significant problem of missed findings in radiology studies. An important issue is assessing the economic benefits of implementing artificial intelligence. AIM: To evaluate the frequency of missed pathologies detection and the economic potential of artificial intelligence technology for chest computed tomography compared and validated by experienced radiologists. MATERIALS AND METHODS: This was an observational, single-center retrospective study. The study included chest computed tomography without IV contrast from June 1 to July 31, 2022, in Clinical Hospital in Yauza, Moscow. The computed tomography was processed using a complex artificial intelligence algorithm for 10 pathologies: pulmonary infiltrates, typical for viral pneumonia (COVID-19 in pandemic conditions); lung nodules; pleural effusion; pulmonary emphysema; thoracic aortic dilatation; pulmonary trunk dilatation; coronary artery calcification; adrenal hyperplasia; and osteoporosis (vertebral body height and density changes). Two experts analyzed computed tomography and compared results with artificial intelligence. Further routing was determined according to clinical guidelines for all findings initially detected and missed by radiologists. The hospital price list determined the potential revenue loss for each patient. RESULTS: From the final 160 computed tomographies, the artificial intelligence identified 90 studies (56%) with pathologies, of which 81 (51%) were missing at least one pathology in the report. The second-stage lost potential revenue for all pathologies from 81 patients was RUB 2,847,760 ($37,251 or CNY 256,218). Lost potential revenue only for those pathologies missed by radiologists but detected by artificial intelligence was RUB 2,065,360 ($27,017 or CNY 185,824). CONCLUSION: Using artificial intelligence as an assistant to the radiologist for chest computed tomography can dramatically minimize the number of missed abnormalities. Compared with the normal model without artificial intelligence, using artificial intelligence can provide 3.6 times more benefits. Using advanced artificial intelligence for chest computed tomography can save money

Erratum in “Volumetry versus linear diameter lung nodule measurement: an ultra-low-dose computed tomography lung cancer screening study” (doi: 10.17816/DD117481)

In the article "Volumetry versus linear diameter lung nodule measurement: an ultra-low-dose computed tomography lung cancer screening study" published in Digital Diagnostics journal Volume 4 Issue 1 in 2023 (doi: 10.17816/DD117481) contained an error in the paragraph with data of funding sources for the study. At the request of the authors team, the error was eliminated, the original version of the published article and the information on the journals site was replaced with the corrected one. Correct text of the changed: This paper was prepared by a group of authors as part of the research work (USIS No. 123031400009-1) in accordance with the Order issued by the Moscow Health Care Department No. 1196 dated December 21, 2022. The authors and the publisher apologize to readers for the published error and express their confidence that this mistake could not significantly affect the perception and interpretation of the results of the study described in the text of the article

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

OBJECTIVE: To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists. METHODS: 283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3. RESULTS: 1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively. CONCLUSION: Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%