Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients: A retrospective study

doi:10.5847/wjem.j.1920-8642.2022.026

Abstract

Abstract:

BACKGROUND: Computed tomography (CT) is a noninvasive imaging approach to assist the early diagnosis of pneumonia. However, coronavirus disease 2019 (COVID-19) shares similar imaging features with other types of pneumonia, which makes differential diagnosis problematic. Artificial intelligence (AI) has been proven successful in the medical imaging field, which has helped disease identification. However, whether AI can be used to identify the severity of COVID-19 is still underdetermined.
METHODS: Data were extracted from 140 patients with confirmed COVID-19. The severity of COVID-19 patients (severe vs. non-severe) was defined at admission, according to American Thoracic Society (ATS) guidelines for community-acquired pneumonia (CAP). The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co., Ltd. was used as the analysis tool to analyze chest CT images.
RESULTS: A total of 117 diagnosed cases were enrolled, with 40 severe cases and 77 non-severe cases. Severe patients had more dyspnea symptoms on admission (12 vs. 3), higher acute physiology and chronic health evaluation (APACHE) II (9 vs. 4) and sequential organ failure assessment (SOFA) (3 vs. 1) scores, as well as higher CT semiquantitative rating scores (4 vs. 1) and AI-CT rating scores than non-severe patients (P<0.001). The AI-CT score was more predictive of the severity of COVID-19 (AUC=0.929), and ground-glass opacity (GGO) was more predictive of further intubation and mechanical ventilation (AUC=0.836). Furthermore, the CT semiquantitative score was linearly associated with the AI-CT rating system (Adj R²=75.5%, P<0.001).
CONCLUSIONS: AI technology could be used to evaluate disease severity in COVID-19 patients. Although it could not be considered an independent factor, there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

Key words: COVID-19, Artificial intelligence, Chest computed tomography

Yi Han, Su-cheng Mu, Hai-dong Zhang, Wei Wei, Xing-yue Wu, Chao-yuan Jin, Guo-rong Gu, Bao-jun Xie, Chao-yang Tong. Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients: A retrospective study[J]. World Journal of Emergency Medicine, 2022, 13(2): 91-97.

Figures/Tables 6

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Parameters	All patients (n=117)	Non-severe patients (n=77)	Severe patients (n=40)	P value
Age (years)	63.5 (48.8-70.2)	62.0 (44.0-69.0)	69.0 (57.5-75.0)	0.002
Gender	-	-	-	0.147
Male	67 (57.3)	40 (51.9)	27 (67.5)
Female	50 (42.7)	37 (48.1)	13 (32.5)
History
Hypertension	32 (27.3)	16 (20.8)	16 (40.0)	0.034
DM	20 (17.1)	14 (18.2)	6 (15.0)	0.625
CHD	6 (5.1)	4 (5.2)	2 (5.0)	0.941
Symptoms
Highest temperature (℃)	38.1 (37.7-38.7)	38.0 (37.2-38.6)	38.4 (38.0-38.9)	0.150
Cough	69 (59.0)	49 (63.6)	20 (50.0)	0.119
Dyspnea	25 (21.4)	13 (16.9)	12 (30.0)	0.117
Length of stay (d)	25.5 (12.75-37.00)	20.0 (10.0-28.0)	38.0 (31.5-43.5)	<0.001
APACHE II	6.0 (3.0-8.0)	4.0 (2.0-6.0)	9.0 (7.0-11.0)	<0.001
SOFA	1.0 (1.0-2.0)	1.0 (0.0-1.0)	3.0 (2.0-4.0)	<0.001
PSI	67.45±32.71	56.03±26.26	88.63±33.19	<0.001
CURB-65	1.0 (0.0-1.0)	0.0 (0.0-1.0)	1.0 (1.0-2.0)	<0.001
CT score	2.0 (1.0-4.0)	1.0 (1.0-3.0)	4.0 (3.0-5.0)	<0.001
AI-CT score	8.86 (2.32-36.61)	4.59 (1.04-10.97)	49.71 (27.87-60.25)	<0.001
Oxygenation
Nasal tube	88 (75.2)	48 (62.3)	40 (100.0)	<0.001
Facial mask	45 (38.5)	8 (10.4)	37 (92.5)	<0.001
HFNC	33 (28.2)	3 (3.9)	30 (75.0)	<0.001
NIV	15 (12.8)	0 (0.0)	15 (37.5)	<0.001
MV	11 (9.4)	0 (0.0)	11 (27.5)	<0.001
ECMO	1 (0.9)	0 (0.0)	1 (2.5)	0.319
Discharge	83 (70.9)	71 (92.2)	12 (30.0)	<0.001
Mortality	8 (6.8)	4 (5.2)	4 (10.0)	0.555

Parameters	All patients (n=117)	Non-severe patients (n=77)	Severe patients (n=40)	P value
White blood cell (×10⁹/L)	5.81 (4.23-8.02)	5.52 (4.22-7.38)	7.14 (4.40-10.70)	0.150
Neutrophil (×10⁹/L)	3.88 (2.69-6.34)	3.41 (2.27-4.85)	5.98 (3.63-9.63)	<0.001
Lymphocyte (×10⁹/L)	1.04 (0.68-1.50)	1.28 (0.93-1.69)	0.68 (0.52-0.92)	<0.001
Monocyte (×10⁹/L)	0.48±0.21	0.52±0.20	0.41±0.21	0.004
CD3⁺ (/μL)	565 (341-858)	769 (527-1065)	365 (304-540)	<0.001
CD4⁺ (/μL)	348 (228-544)	466 (314-649)	236 (170-314)	<0.001
CD8⁺ (/μL)	192 (118-318)	238 (170-367)	140 (74-186)	<0.001
CD19 (/μL)	128 (77-203)	163 (74-211)	111 (77-174)	0.223
CD16+56 (/μL)	112.5 (65.8-185.8)	130 (78-203)	100 (62-162)	0.085
CRP (mg/L)	27.8 (5.0-77.5)	8.1 (5.0-50.9)	103.8 (30.2-175.3)	<0.001
PaO₂/FiO₂ (mmHg)	240.39±147.17	330.69±162.12	215.68±143.27	0.016
IL-2 (pg/mL)	3.62 (3.17-4.08)	3.62 (3.17-4.12)	3.54 (3.13-4.06)	0.646
IL-4 (pg/mL)	3.21 (2.90-3.79)	3.21 (2.84-3.91)	3.22 (2.98-3.63)	0.895
IL-6 (pg/mL)	8.24 (5.59-22.79)	7.90 (4.57-10.92)	16.11 (7.59-102.31)	0.002
IL-10 (pg/mL)	5.91 (4.69-7.95)	5.34 (4.44-6.42)	7.86 (5.46-13.53)	0.005
TNF-α (pg/mL)	3.21 (2.68-5.01)	3.46 (2.66-5.08)	3.18 (2.71-4.01)	0.491
IFN-γ (pg/mL)	3.63 (2.80-5.40)	3.85 (2.71-6.02)	3.47 (2.96-4.33)	0.569
PCT (ng/mL)	0.10 (0.04-0.21)	0.04 (0.03-0.12)	0.19 (0.12-0.40)	<0.001