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Differences Among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology

Differences Among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology

Journal of Personalized Medicine

Article Differences among COVID-19, and Atypical in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology

Robert Chrzan 1,* , Monika Boci ˛aga-Jasik 2, Amira Bryll 1, Anna Grochowska 1 and Tadeusz Popiela 1

1 Department of Radiology, Jagiellonian University Medical College, Kopernika 19, 31-501 Krakow, Poland; [email protected] (A.B.); [email protected] (A.G.); [email protected] (T.P.) 2 Department of Infectious Diseases, Jagiellonian University Medical College, Jakubowskiego 2, 30-688 Krakow, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-124-00-14-00

Abstract: The aim of this study was to compare the results of automatic assessment of high resolution computed tomography (HRCT) by artificial intelligence (AI) in 150 patients from three subgroups: pneumonia in the course of COVID-19, bronchopneumonia and . The volume percentage of inflammation and the volume percentage of “ground glass” were significantly higher in the atypical (respectively, 11.04%, 8.61%) and the COVID-19 (12.41%, 10.41%) subgroups compared to the bronchopneumonia (5.12%, 3.42%) subgroup. The volume percentage of consolidation was   significantly higher in the COVID-19 (2.95%) subgroup compared to the atypical (1.26%) subgroup. The percentage of “ground glass” in the volume of inflammation was significantly higher in the Citation: Chrzan, R.; Boci ˛aga-Jasik, M.; atypical (89.85%) subgroup compared to the COVID-19 (79.06%) subgroup, which in turn was Bryll, A.; Grochowska, A.; Popiela, T. significantly higher compared to the bronchopneumonia (68.26%) subgroup. HRCT chest images, Differences among COVID-19, analyzed automatically by artificial intelligence software, taking into account the structure including Bronchopneumonia and Atypical “ground glass” and consolidation, significantly differ in three subgroups: COVID-19 pneumonia, Pneumonia in Chest High Resolution bronchopneumonia and atypical pneumonia. However, the partial overlap, particularly between Computed Tomography Assessed by COVID-19 pneumonia and atypical pneumonia, may limit the usefulness of automatic analysis in Artificial Intelligence Technology. J. Pers. Med. 2021, 11, 391. https:// differentiating the etiology. In our future research, we plan to use artificial intelligence for objective doi.org/10.3390/jpm11050391 assessment of the dynamics of pulmonary lesions during COVID-19 pneumonia.

Academic Editor: Keywords: COVID-19; HRCT; artificial intelligence; ground glass Ali Salehzadeh-Yazdi

Received: 21 March 2021 Accepted: 6 May 2021 1. Introduction Published: 10 May 2021 At the beginning of the COVID-19 pandemic, high hopes were placed for using diagnostic imaging, in particular chest HRCT (high resolution computed tomography), to Publisher’s Note: MDPI stays neutral detect positive cases. However, it turned out that the specificity is limited, because the with regard to jurisdictional claims in symptoms observed may occur in of other etiologies. published maps and institutional affil- According to the current recommendations of radiological societies (British Thoracic iations. Imaging Society [1], American College of Radiology [2]), chest HRCT should not be used as a screening tool or as a first-line test in the diagnosis of COVID-19 . A PCR (polymerase chain reaction) test of a nasopharyngeal or pharyngeal swab remains the basic method of verifying COVID-19. However, chest HRCT is now often used to assess the Copyright: © 2021 by the authors. complications in COVID-19 patients. Licensee MDPI, Basel, Switzerland. Artificial intelligence technology is currently of great interest in medical imaging. This article is an open access article Software that automatically recognizes abnormal structures on X-rays or CT images can be distributed under the terms and a very valuable aid in the daily work of a radiologist [3,4]. conditions of the Creative Commons In the course of the pandemic, the main reason for the interest in the assessment Attribution (CC BY) license (https:// of HRCT images by artificial intelligence was the ability to analyze large numbers of creativecommons.org/licenses/by/ 4.0/). patients in a short time [5], which was particularly important in centers with an insufficient

J. Pers. Med. 2021, 11, 391. https://doi.org/10.3390/jpm11050391 https://www.mdpi.com/journal/jpm J. Pers. Med. 2021, 11, 391 2 of 10

number of qualified radiologists. However, the question quickly arose as to whether such technology could accurately identify COVID-19 pneumonia and differentiate it from pneumonia of a different etiology [5,6]. Symptomatic COVID-19 patients typically have respiratory and systemic manifesta- tions. COVID-19 pneumonia most commonly presents with , , produc- tion and . The primary findings in HRCT are: “ground glass” opacities (usually bilateral, peripheral and subpleural), crazy paving pattern, consolidations and bronchovascular thickening [7,8]. Bronchopneumonia, also known as lobular pneumonia, is the most common type of pneumonia, caused by , such as , pneumoniae, Haemophilus influenzae, , and , and less often anaerobes, such as Proteus species. Clinically, bronchopneumonia may present with productive cough, , dyspnea, fever, shivers, pleuritic pain and occasionally . Suppurative peribronchiolar inflammation and subsequent patchy consolidation of one or more sec- ondary lobules create an HRCT pattern of multiple focal opacities in a lobular pattern, centered at centrilobular (a tree-in-bud appearance) [9]. Such small foci can merge into larger heterogeneous confluent areas of consolidation. “Atypical” pneumonia is a type of pneumonia that is not caused by one of the most commonly associated with the disease and with clinical presentation different from “typical” pneumonia. The most common that cause atypical pneumonia are: “atypical” bacteria, such as pneumoniae, pneumoniae, , , , and pneumophila, and , such as respiratory syncytial (RSV), influenza A and B, parainfluenza, ade- novirus, severe respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), or even fungi and . It is clinically characterized by a low-grade fever, a moderate amount of sputum, a persistent dry cough, more pronounced general symptoms, such as a and , no response to sulfonamide and beta-lactams, no , no symptoms of lobar consolidation and lack of alveolar . The inflammation is usually limited to the pulmonary interstitium and the interlobular septa; therefore, atypical pneumonia usually has a pattern of focal or diffuse bilateral “ground glass” opacities in HRCT [9]. The aim of the study was to compare the results of automatic assessment of HRCT images by a software, using artificial intelligence technology, in three subgroups of patients: with pneu- monia in the course of COVID-19, with bronchopneumonia and with “atypical” pneumonia. A new idea in our research was to use artificial intelligence, not to simply distin- guish COVID-19 pneumonia from pneumonia of other causes, but to automatically detect pulmonary lesions and assess their structure, including the areas of “ground glass” and con- solidations, to finally compare such structure in three subgroups of pneumonia as above.

2. Materials and Methods The HIS (hospital information system) and the hospital PACS (picture archiving and communication system) were retrospectively searched for patients with clinical symptoms of pneumonia confirmed in chest HRCT examinations. Patients with pre-existing chronic lung diseases, including severe emphysema and interstitial lung diseases, were excluded from the study. Three subgroups of patients were analyzed: 1. The patients with pneumonia in the course of COVID-19 infection were confirmed by PCR from nasopharyngeal swabs. Fifty patients from the period of 06.04.2020–12.06.2020 were selected, which resulted in a subgroup consisting of 18 females, 32 males, 34–86 years old, average of 60 years old; 2. The patients with bronchopneumonia were diagnosed both in the patients’ clin- ical documentation and radiological assessment. Fifty patients from the period of 11.03.2013–03.06.2020 were selected, which resulted in a subgroup consisting of 20 females, 30 males, 28–97 years old, average of 65 years old; J. Pers. Med. 2021, 11, 391 3 of 10

3. The patients with “atypical” pneumonia were diagnosed both in the patients’ clin- ical documentation and radiological assessment. Fifty patients from the period of 03.09.2010–09.01.2020 were selected, which resulted in a subgroup consisting of 18 females, 32 males, 20–80 years old, average of 55 years old. According to clinical data, the virus was the etiological factor in 21 cases, including 4 cases of influenza, 2 cases of (CMV), 1 case of respiratory syncytial virus (RSR) and the remaining 14 cases were not specified; Pneumocystis jiroveci (PCP) in 8 cases; in 1 case; in 1 case; and in the remaining t of clinically and radiologically diagnosed atypical pneumonia, no etiological factor was specified in the available documentation. All the HRCT examinations were performed using multirow (64 or 80 rows) heli- cal scanners, slice thickness of 0.625 mm, 0.75 mm, 1 mm or 1.25 mm, mainly 120 kV, 100–350 mAs. For every patient, the HRCT images in original DICOM (Digital Imaging and Com- munications in Medicine) format were transferred from PACS to the processing server YITU Healthcare set up and configured in the local hospital network. The server software was developed by YITU CT, YITU Healthcare Technology Co., Ltd. (Shanghai, China) in cooperation with Huawei Technologies Co., Ltd., China (Shenzhen, China) [10,11]. The images were automatically analyzed by the software, using artificial intelligence technol- ogy. The examples of analysis of atypical pneumonia, bronchopneumonia and COVID-19 pneumonia are presented in Figure1.

Figure 1. The examples of analysis of (A) atypical pneumonia, (B) bronchopneumonia and (C) COVID-19 pneumonia.

The final report from the assessment was available after login into the server (Figure2). The report included, i.a.: • Inflammation CT density histogram, the values of average, median, and peak CT density for the inflammation regions in the right, the left and both ; • Inflammation (“Lesion”) volume, in cubic centimeters and as a percentage of the whole lung tissue, for the right, the left and both lungs; • Within the inflammation volume as above, distinction into “Ground glass” and “Consolidation”(Lesion = Ground glass + Consolidation), in cubic centimeters and as a percentage of the whole lung tissue, for the right, the left and both lungs; • The HRCT images with regions of inflammation marked in color; • Inflammation volume as a percentage of the whole lung tissue, with distinction into “Ground glass” and “Consolidation”, independently for every lung lobe, additionally with a “pseudo 3D” graphic presentation; • Estimated risk of pneumonia, assessed by artificial intelligence software based on CT evaluation, reported as mild, moderate or critical (“Suspected pneumonia”); • Preprepared text of radiological report. J. Pers. Med. 2021, 11, 391 4 of 10 J. Pers. Med. 2021, 11, x FOR PEER REVIEW 4 of 10

Figure 2. 2. The Thefinal finalreport reportfrom automatic from automatic assessment assessment of HRCT imag of HRCTes by the images software, by using the software,artificial intelligence using artificial tech- nology.intelligence technology.

TheIt was report also included, possible to i.a.: export all the assessment results from the processing server to •the ExcelInflammation spreadsheet. CT density histogram, the values of average, median, and peak CT densityFor the for purposes the of this study, regions the followingin the right, parameters the left and were both analyzed lungs; in every •patient: InflammationLesion volume (“Lesion Both”) volume, lungs (%) in, cubic the volume centimeters of inflammation and as a percentage (“Lesion”) of asthe a percentagewhole oflung the tissue, volume for of the both right, lungs; theGround left and glass both volumelungs; Both lungs (%), the volume of “Ground glass” as a percentage of the volume of both lungs; Consolidation volume • BothWithin lungs (%)the, theinflammation volume of consolidationvolume as above, as a percentage distinction of into the volume“Ground of glass both” lungs; and Ground“Consolidation glass volume/Lesion” (Lesion = volume Ground Both glass lungs + Consolidation (%), the volume), in cubic of “Ground centimeters glass ”and as a percentageas a percentage of the of volume the whole of inflammation lung tissue, for (“ Lesionthe right,”) inthe both left and lungs; both and lungs;Suspected •pneumonia The HRCT, the images estimated with risk regions of pneumonia. of inflammation marked in color; • InflammationStatistical software volume R as 4.0.3 a percentage (Vienna, Austria) of the whole [12] waslung used tissue, for with computations distinction byinto a certified“Ground statistician. glass” and “Consolidation”, independently for every lung lobe, addition- allyThe with comparison a “pseudo of the 3D” values graphic of quantitative presentation; variables in three groups was performed using the Kruskal–Wallis test. After detecting statistically significant differences, post-hoc • Estimated risk of pneumonia, assessed by artificial intelligence software based on CT analysis with Dunn’s test was performed to identify significantly different groups. evaluation, reported as mild, moderate or critical (“Suspected pneumonia”); The comparison of the values of qualitative variables in the groups was performed • using Preprepared the chi-squared text of test. radiological report. ItThe was significance also possible level to forexport allstatistical all the assessment tests was results set to 0.05. from the processing server to the ExcelThe studyspreadsheet. was approved by the local bioethics committee (opinion No. 1072.6120.333.2020 of 7 DecemberFor the purposes 2020). of this study, the following parameters were analyzed in every pa- tient: Lesion volume Both lungs (%), the volume of inflammation (“Lesion”) as a per- centage3. Results of the volume of both lungs; Ground glass volume Both lungs (%), the volume of “GroundLesion glass volume” as Both a percentage lungs (%) of wasthe volume significantly of both higher lungs; in Consolidation the atypical (medianvolume Both11.04%) lungs and (%) the, COVID-19the volume (median of consolidation 12.41%) subgroupsas a percentage compared of the to volume the bronchopneumo- of both lungs; Groundnia (median glass 5.12%) volume/Lesion subgroup volume (Figure 3Both). Partial lungs overlap (%), the between volume theof “ threeGround pneumonia glass” as asubgroups percentage was of present.the volume of inflammation (“Lesion”) in both lungs; and Suspected pneumonia, the estimated risk of pneumonia.

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Statistical software R 4.0.3 (Vienna, Austria) [12] was used for computations by a cer- Statistical software R 4.0.3 (Vienna, Austria) [12] was used for computations by a cer- tified statistician. tified statistician. The comparison of the values of quantitative variables in three groups was per- The comparison of the values of quantitative variables in three groups was per- formed using the Kruskal–Wallis test. After detecting statistically significant differences, formed using the Kruskal–Wallis test. After detecting statistically significant differences, post-hoc analysis with Dunn’s test was performed to identify significantly different post-hoc analysis with Dunn’s test was performed to identify significantly different groups. groups. The comparison of the values of qualitative variables in the groups was performed The comparison of the values of qualitative variables in the groups was performed using the chi-squared test. using the chi-squared test. The significance level for all statistical tests was set to 0.05. The significance level for all statistical tests was set to 0.05. The study was approved by the local bioethics committee (opinion No. The study was approved by the local bioethics committee (opinion No. 1072.6120.333.2020 of 7 December 2020). 1072.6120.333.2020 of 7 December 2020). 3. Results 3. Results Lesion volume Both lungs (%) was significantly higher in the atypical (median Lesion volume Both lungs (%) was significantly higher in the atypical (median 11.04%) and the COVID-19 (median 12.41%) subgroups compared to the bronchopneu- 11.04%) and the COVID-19 (median 12.41%) subgroups compared to the bronchopneu- monia (median 5.12%) subgroup (Figure 3). Partial overlap between the three pneumonia J. Pers. Med. 2021, 11, 391 monia (median 5.12%) subgroup (Figure 3). Partial overlap between the three pneumonia5 of 10 subgroups was present. subgroups was present.

Figure 3. Comparison of the volume of inflammation (“lesion”) as a percentage of the volume of Figure 3. Comparison of the volume of inflammation (“lesion”) as a percentage of the volume of Figureboth lungs, 3. Comparison in three subgroups of the volume of pneumonia. of inflammation (“lesion”) as a percentage of the volume of bothboth lungs, lungs, in three in three subgroups subgroups of pneumonia. of pneumonia. Ground glass volume Both lungs (%) was significantly higher in the atypical (me- GroundGround glass glass volume volume Both Both lungs lungs (%) (%) waswas significantly significantly higher higher in in the the atypical (median(me- dian 8.61%) and the COVID-19 (median 10.41%) subgroups compared to the broncho- dian8.61%) 8.61%) and and the the COVID-19 COVID-19 (median (median 10.41%) 10.41%) subgroups subgroups compared compared to the to bronchopneumo-the broncho- pneumonia (median 3.42%) subgroup (Figure 4). Partial overlap between the three pneu- pneumoniania (median (median 3.42%) 3.42%) subgroup subgroup (Figure (Figure4). Partial 4). Partial overlap overlap between between the the three three pneumonia pneu- monia subgroups was present. moniasubgroups subgroups was was present. present.

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FigureFigure 4. Comparison 4. Comparison of the of thevolume volume of “ground of “ground glass” glass” as a as percentage a percentage of the of thevolume volume of both of both lungs, Figurelungs,in three in4. Comparisonthree subgroups subgroups of of the pneumonia. of volumepneumonia. of “ground glass” as a percentage of the volume of both lungs, in three subgroups of pneumonia. Consolidation volume Both lungs (%) was significantly higher in the COVID-19 (median 2.95%)Consolidation subgroup volume compared Both tolungs the atypical (%) was (median significantly 1.26%) highersubgroup in the(Figure COVID-19 5). Partial(median overlap 2.95%) between subgroup the three compared pneumonia to the subgroups atypical (medianwas present. 1.26%) subgroup (Figure5). Partial overlap between the three pneumonia subgroups was present.

FigureFigure 5. Comparison 5. Comparison of the of volume the volume of consolidation of consolidation as a aspercentage a percentage of the of volume the volume of both of both lungs, lungs, in in threethree subgroups subgroups of ofpneumonia. pneumonia.

Ground glass volume/Lesion volume Both lungs (%) was significantly higher in the atypical (median 89.85%) subgroup compared to the COVID-19 (median 79.06%) sub- group, which in turn was significantly higher compared to the bronchopneumonia (me- dian 68.26%) subgroup (Figure 6). However, partial overlap between the three pneumonia subgroups, particularly in COVID-19 pneumonia and atypical pneumonia, was found again.

Figure 6. Comparison of the volume of “ground glass” as a percentage of the volume of inflamma- tion (“lesion”) in both lungs, in three subgroups of pneumonia.

Suspected pneumonia—the greatest, estimated by the software, risk of pneumonia, based on HRCT images, was in the COVID-19 (56% of cases assessed as critical risk) sub- group, and the lowest risk was in the bronchopneumonia (22% of cases assessed as critical risk) subgroup (Figure 7).

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Consolidation volume Both lungs (%) was significantly higher in the COVID-19 (median 2.95%) subgroup compared to the atypical (median 1.26%) subgroup (Figure 5). Partial overlap between the three pneumonia subgroups was present.

Figure 5. Comparison of the volume of consolidation as a percentage of the volume of both lungs, J. Pers. Med. 2021, 11, 391 in three subgroups of pneumonia. 6 of 10

Ground glass volume/Lesion volume Both lungs (%) was significantly higher in the atypical (median 89.85%) subgroup compared to the COVID-19 (median 79.06%) sub- Ground glass volume/Lesion volume Both lungs (%) was significantly higher in the group, which in turn was significantly higher compared to the bronchopneumonia (me- atypical (median 89.85%) subgroup compared to the COVID-19 (median 79.06%) subgroup, dian 68.26%) subgroup (Figure 6). However, partial overlap between the three pneumonia which in turn was significantly higher compared to the bronchopneumonia (median 68.26%) subgroups, particularly in COVID-19 pneumonia and atypical pneumonia, was found subgroup (Figure6). However, partial overlap between the three pneumonia subgroups, again. particularly in COVID-19 pneumonia and atypical pneumonia, was found again.

FigureFigure 6. Comparison 6. Comparison of the of thevolume volume of “ground of “ground glass” glass” as asa percentage a percentage of of the the volume volume of of inflamma- inflammation tion(“lesion”) (“lesion”) in in both both lungs, lungs, in in three three subgroups subgroups of of pneumonia. pneumonia.

SuspectedSuspected pneumonia— pneumoniathe—the greatest, greatest, estimated estimated by the by thesoftware, software, risk risk of pneumonia, of pneumonia, J. Pers. Med. 2021, 11, x FOR PEER REVIEW based on HRCT images, was in the COVID-19 (56% of cases assessed as critical7 of risk)10 based on HRCT images, was in the COVID-19 (56% of cases assessed as critical risk) sub- group,subgroup, and the andlowest the risk lowest was riskin the was bronchopneumonia in the bronchopneumonia (22% of cases (22% assessed of cases as assessed critical as risk)critical subgroup risk) (Figure subgroup 7). (Figure7).

Figure 7. ComparisonFigure of 7.theComparison estimated of therisk estimated of pneumo risk ofnia, pneumonia, based on based HRCT on HRCT images, images, in inthree three sub- subgroups of pneumonia. groups of pneumonia. The results of the statistical analysis are summarized in Table1. The results of the statistical analysis are summarized in Table 1.

Table 1. The results of statistical analysis of parameters reported by the software after automatic assessment of HRCT images, in three subgroups of pneumonia.

Parameter Pneumonia p Atypical Broncho- COVID-19 (N = 50) -

(N = 50) - A pneumonia (N = 50) - B C Mean ± SD 16.68 ± 17.02 8.96 ± 10.33 21.18 ± 19.66 p = 0.001 Lesion volume Both lungs (%) Median 11.04 5.12 12.41 Quartiles 4.72–22.04 1.88–12.45 5.01–32.55 C, A > B Mean ± SD 14.05 ± 14.05 6.22 ± 7.71 15.87 ± 14.55 p < 0.001 Ground glass volume Both lungs (%) Median 8.61 3.42 10.41 Quartiles 3.78–17.85 1.35–8.51 3.97–25.88 C, A > B Mean ± SD 2.63 ± 3.88 2.75 ± 2.91 5.31±6.35 p = 0.05 Consolidation volume Both lungs (%) Median 1.26 1.54 2.95 Quartiles 0.26–3.19 0.62–4.11 0.64–6.34 C > A Mean ± SD 86.28 ± 10.65 67.74 ± 12.4 78.15 ± 12.12 p < 0.001 Ground glass volume/Lesion volume Both lungs (%) Median 89.85 68.26 79.06 Quartiles 81.22–94.89 60.68–77.26 72.12–86.95 A > C > B Mild 16 (32%) 14 (28%) 7 (14%) p = 0.005 Suspected pneumonia Moderate 14 (28%) 25 (50%) 15 (30%) Critical 20 (40%) 11 (22%) 28 (56%)

4. Discussion In recent years, artificial intelligence technology has found more and more applica- tions in the diagnostics of lung diseases, including lung nodules [3], emphysema [4] and lung [13]. After the outbreak of the COVID-19 pandemic with the dominant lung clinical symp- toms, research on the use of artificial intelligence technology in the assessment of chest CT in such patients was quickly launched [14–18]. In our study, the volume of “ground glass” as a percentage of the volume of inflam- mation in both lungs was significantly higher in the atypical (median 89.85%) subgroup compared to the COVID-19 (median 79.06%) subgroup, which in turn was significantly higher compared to the bronchopneumonia (median 68.26%) subgroup. However, we found partial overlap, particularly between the COVID-19 pneumonia and the atypical pneumonia subgroups. Li [5] developed a deep learning model—a detection neural network—to extract vis- ual features of COVID-19 from chest CT. Using community acquired pneumonia (CAP) and other non-pneumonia CT exams as a control group, they obtained a sensitivity of 90%, a specificity of 96% and an AUC (area under the receiver operating characteristic curve) of 0.96 for detecting COVID-19. However, they noted the overlap in the chest CT

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Table 1. The results of statistical analysis of parameters reported by the software after automatic assessment of HRCT images, in three subgroups of pneumonia.

Parameter Pneumonia p Atypical Broncho- COVID-19 (N = 50) - C (N = 50) - A pneumonia (N = 50) - B Mean ± SD 16.68 ± 17.02 8.96 ± 10.33 21.18 ± 19.66 p = 0.001 Lesion volume Both lungs (%) Median 11.04 5.12 12.41 Quartiles 4.72–22.04 1.88–12.45 5.01–32.55 C, A > B Mean ± SD 14.05 ± 14.05 6.22 ± 7.71 15.87 ± 14.55 p < 0.001 Ground glass volume Both Median 8.61 3.42 10.41 lungs (%) Quartiles 3.78–17.85 1.35–8.51 3.97–25.88 C, A > B Mean ± SD 2.63 ± 3.88 2.75 ± 2.91 5.31±6.35 p = 0.05 Consolidation volume Both Median 1.26 1.54 2.95 lungs (%) Quartiles 0.26–3.19 0.62–4.11 0.64–6.34 C > A Mean ± SD 86.28 ± 10.65 67.74 ± 12.4 78.15 ± 12.12 p < 0.001 Ground glass volume/Lesion Median 89.85 68.26 79.06 volume Both lungs (%) Quartiles 81.22–94.89 60.68–77.26 72.12–86.95 A > C > B Mild 16 (32%) 14 (28%) 7 (14%) p = 0.005 Suspected pneumonia Moderate 14 (28%) 25 (50%) 15 (30%) Critical 20 (40%) 11 (22%) 28 (56%)

4. Discussion In recent years, artificial intelligence technology has found more and more applications in the diagnostics of lung diseases, including lung nodules [3], emphysema [4] and [13]. After the outbreak of the COVID-19 pandemic with the dominant lung clinical symp- toms, research on the use of artificial intelligence technology in the assessment of chest CT in such patients was quickly launched [14–18]. In our study, the volume of “ground glass” as a percentage of the volume of inflam- mation in both lungs was significantly higher in the atypical (median 89.85%) subgroup compared to the COVID-19 (median 79.06%) subgroup, which in turn was significantly higher compared to the bronchopneumonia (median 68.26%) subgroup. However, we found partial overlap, particularly between the COVID-19 pneumonia and the atypical pneumonia subgroups. Li [5] developed a deep learning model—a detection neural network—to extract visual features of COVID-19 from chest CT. Using community acquired pneumonia (CAP) and other non-pneumonia CT exams as a control group, they obtained a sensitivity of 90%, a specificity of 96% and an AUC (area under the receiver operating characteristic curve) of 0.96 for detecting COVID-19. However, they noted the overlap in the chest CT imaging findings of all viral and other chest diseases, encouraging a multidisciplinary approach to the final diagnosis. It is similar to our observation of overlap as above. Duzgun [19] even calls COVID-19 pneumonia “the great radiological mimicker”, emphasizing that CT findings in such pneumonia may be non-specific and variable during the disease course, resembling numerous infectious and non-infectious diseases, including , hemorrhage, neoplasms, organizing pneumonia, pulmonary alveolar proteinosis, , pulmonary infarction, interstitial lung diseases and . It also confirms our finding of partial overlap. Bai [20] assessed the performance of six radiologists in differentiating COVID-19 from at chest CT. Sensitivity ranged from 72% to 94%, and specificity demonstrated a high variation (24–100%). CT features more commonly in COVID-19 pneumonia than in non-COVID-19 viral pneumonia, including peripheral distribution, ground-glass opacity, fine reticular opacity, vascular thickening and reverse halo sign. In another study, Bai [6] demonstrated that the use of artificial intelligence assistance improved the accuracy in distinguishing COVID-19 pneumonia from pneumonia of other causes on chest CT scans. Without artificial intelligence prediction, six radiologists had J. Pers. Med. 2021, 11, 391 8 of 10

an average accuracy of 85%, an average sensitivity of 79% and an average specificity of 88%. Assisted by the artificial intelligence, the radiologists achieved an accuracy of 90%, a sensitivity of 88% and a specificity of 91%. The software used in our study was not trained to literally distinguish COVID-19 pneumonia from pneumonia of other causes, but to automatically detect pulmonary lesions and assess their structure. Therefore, we could not calculate sensitivity and specificity to directly compare our results with the values presented by other authors as above. Caruso [21] performed quantitative analysis in discriminating COVID-19 from non- COVID-19 patients with chest CT suggestive for interstitial pneumonia. Lung quantifica- tion in liters showed significant differences between COVID-19 and non-COVID-19 patients for “ground glass” opacities (GGO) (0.55 ± 0.26 L vs. 0.43 ± 0.23 L) and fibrotic alterations (0.05 ± 0.03 L vs. 0.04 ± 0.03 L). It is difficult to compare these values with our results because Caruso used absolute volumes, and we used relative “ground glass” volumes—as a percentage of the volume of both lungs or as a percentage of the volume of inflammation. In our study, “ground glass” volume as a percentage of the volume of inflammation in the COVID-19 subgroup was significantly higher compared to the bronchopneumonia subgroup, but, contrary to Caruso’s results, lower compared to the atypical subgroup. Li [7] presented the spectrum of chest CT manifestations in COVID-19 infection, taking into account the temporal progression of the disease, with the distinction of four phases. In the early phase, the lesions are limited to single or multiple areas of nodular or patchy ground glass lesions. In the progressive phase, the number, extent and density of lesions increase significantly, with coexistence of ground glass areas and consolidations. In the severe phase, peaking around 14 days after the onset of the disease, diffuse infiltration of all segments of the lungs (“white lung”) is visible. Finally, in the dissipative phase, gradual absorption of the lesions is observed, leaving a few cord-like high-density structures, indicative of fibrosis. Thus, ground glass areas, commonly considered typical symptoms of COVID-19 infection, are observed alone only in the early phase—later accompanied by consolidations and diffuse infiltrations. In our study, the patients with pneumonia in the course of COVID-19 infection were examined by chest CT in different phases of the disease. That may explain why the volume of consolidation as a percentage of the volume of both lungs was even the highest in COVID-19 compared to the atypical and the bronchopneumonia subgroups. In our work, we did not assess the clinical severity of pneumonia, correlated with the volume of parenchymal lesions. It is therefore an important bias that should be taken into account. Interestingly, quantification of COVID-19 pneumonia lesions on CT images, automati- cally calculated using artificial intelligence algorithms, can early and non-invasively predict the progression to the severe form [22]. It provides a promising prognostic indicator for further clinical management of COVID-19 patients. In patients with confirmed COVID-19 infection, such analysis may be a valuable method of objective assessment of the dynamics of pulmonary lesions [23]. We plan to address this topic in our future research.

5. Conclusions The final results are: • The volume percentage of inflammation and the volume percentage of “ground glass” were significantly higher in the atypical and the COVID-19 subgroups compared to the bronchopneumonia subgroup; • The volume percentage of consolidation was significantly higher in the COVID-19 subgroup compared to the atypical subgroup; • The percentage of “ground glass” in the volume of inflammation was significantly higher in the atypical subgroup compared to the COVID-19 subgroup, which in turn was significantly higher compared to the bronchopneumonia subgroup. It confirms the conclusion that HRCT chest images, analyzed automatically by artifi- cial intelligence software, taking into account the structure including “ground glass” and J. Pers. Med. 2021, 11, 391 9 of 10

consolidation, significantly differ in three subgroups: COVID-19 pneumonia, bronchop- neumonia and atypical pneumonia. However, the partial overlap found, particularly between the COVID-19 pneumonia and the atypical pneumonia subgroups, may limit the usefulness of automatic analysis in differentiating the etiology.

Author Contributions: Conceptualization, R.C., M.B.-J. and T.P.; methodology, R.C., M.B.-J. and T.P.; software, R.C.; validation, R.C., A.G. and A.B.; formal analysis, R.C.; investigation, R.C., A.G. and A.B.; resources, R.C., A.G. and A.B.; data curation, R.C. and A.G.; writing—original draft preparation, R.C.; writing—review and editing, R.C.; supervision, M.B.-J. and T.P.; project administration, R.C. and T.P. All authors have read and agreed to the published version of the manuscript. Funding: This publication was supported by the National Center for Research and Development CRACoV-HHS project (Model of multi-specialist hospital and non-hospital care for patients with SARS- CoV-2 infection) through the initiative “Support for specialist hospitals in fighting the spread of SARS- CoV-2 infection and in treating COVID-19” (contract number SZPITALE-JEDNOIMIENNE/18/2020). The described research was implemented by consortium of the University Hospital in Cracow and the Jagiellonian University Medical College. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Bioethics Committee of the Jagiellonian University (opinion No. 1072.6120.333.2020 of 7 December 2020). Informed Consent Statement: Patient consent was waived due to retrospective assessment of CT images, obtained as a part of routine medical procedures. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the hospital procedures. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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